{
"cells": [
{
"cell_type": "markdown",
"id": "aa21cf58",
"metadata": {},
"source": [
"# DeepLabCut Process Position\n",
"\n",
"## Dhruv Mehrotra, 2022\n",
"\n",
"\n",
"In this notebook, we will learn how to analyze position data from a given sub-region of your environment. In particular, this example deals with a mouse running on the radial arm maze, but the idea can be generalized to the analysis of a given sub-region of any environment. \n",
"\n",
"\n",
"Let's get right into it! First, import the necessary libraries."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ee9c3d76",
"metadata": {},
"outputs": [],
"source": [
"import pynapple as nap\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import scipy.io\n",
"import os, sys\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from pylab import *\n"
]
},
{
"cell_type": "markdown",
"id": "1440e2c9",
"metadata": {},
"source": [
"Next, load the data from your directory. My data is being read from an h5 file, but this can be replaced to read whatever format you are working with (csv, MAT file etc)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "39db3389",
"metadata": {},
"outputs": [
{
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" </tr>\n",
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"</table>\n",
"<p>154437 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
"scorer DLC_mobnet_100_unimplantedJan4shuffle1_200000 \\\n",
"bodyparts nose \n",
"coords x y \n",
"0 325.915680 87.834717 \n",
"1 330.679016 92.426193 \n",
"2 330.229675 92.469246 \n",
"3 324.419312 85.861443 \n",
"4 324.482239 87.202263 \n",
"... ... ... \n",
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"154435 328.655090 86.512009 \n",
"154436 991.879517 174.817444 \n",
"\n",
"scorer \\\n",
"bodyparts leftear rightear \n",
"coords likelihood x y likelihood x \n",
"0 0.047951 327.809082 89.872162 0.031015 383.521667 \n",
"1 0.005103 111.054222 1007.833008 0.006857 384.808868 \n",
"2 0.003538 991.470459 573.574402 0.003131 384.428528 \n",
"3 0.028133 328.421967 90.776245 0.030893 384.233154 \n",
"4 0.014028 327.500519 90.704430 0.016099 384.890259 \n",
"... ... ... ... ... ... \n",
"154432 0.001959 423.084625 199.708374 0.001155 384.718536 \n",
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"154436 0.001389 422.413788 196.893265 0.002721 386.354828 \n",
"\n",
"scorer \n",
"bodyparts \n",
"coords y likelihood \n",
"0 158.932144 0.019942 \n",
"1 158.491882 0.008222 \n",
"2 158.548752 0.012223 \n",
"3 157.995850 0.030514 \n",
"4 158.675751 0.016726 \n",
"... ... ... \n",
"154432 157.750381 0.002946 \n",
"154433 157.800858 0.005548 \n",
"154434 157.707367 0.031571 \n",
"154435 158.987778 0.011806 \n",
"154436 157.463684 0.075369 \n",
"\n",
"[154437 rows x 9 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_directory = '/media/DataDhruv/Recordings/unimplanted/211231'\n",
"tracking_data = pd.read_hdf(data_directory + '/' + '1819-211231_1.h5', mode = 'r')\n",
"\n",
"tracking_data\n"
]
},
{
"cell_type": "markdown",
"id": "a3cdd606",
"metadata": {},
"source": [
"Here, we see that tracking_data has 9 columns. Namely, the x and y positions of the 3 bodyparts I labelled (nose, left ear and right ear), as well as the likelihood. We are only interested in the x and y positions, so we will extract these, and create a new DataFrame, with just the relevant data."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e8dba974",
"metadata": {},
"outputs": [
{
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],
"text/plain": [
"scorer DLC_mobnet_100_unimplantedJan4shuffle1_200000 \\\n",
"bodyparts nose \n",
"coords x y \n",
"0 325.915680 87.834717 \n",
"1 330.679016 92.426193 \n",
"2 330.229675 92.469246 \n",
"3 324.419312 85.861443 \n",
"4 324.482239 87.202263 \n",
"... ... ... \n",
"154432 991.857971 175.494125 \n",
"154433 991.476501 174.963913 \n",
"154434 991.720520 175.118958 \n",
"154435 328.655090 86.512009 \n",
"154436 991.879517 174.817444 \n",
"\n",
"scorer \n",
"bodyparts leftear rightear \n",
"coords x y x y \n",
"0 327.809082 89.872162 383.521667 158.932144 \n",
"1 111.054222 1007.833008 384.808868 158.491882 \n",
"2 991.470459 573.574402 384.428528 158.548752 \n",
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"4 327.500519 90.704430 384.890259 158.675751 \n",
"... ... ... ... ... \n",
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"154433 991.325317 573.928833 384.466309 157.800858 \n",
"154434 417.429535 191.197449 386.694214 157.707367 \n",
"154435 314.363403 -13.260569 384.249390 158.987778 \n",
"154436 422.413788 196.893265 386.354828 157.463684 \n",
"\n",
"[154437 rows x 6 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hd_cols = [0,1,3,4,6,7]\n",
"hd_data = tracking_data.iloc[:,hd_cols]\n",
"\n",
"hd_data"
]
},
{
"cell_type": "markdown",
"id": "f97b3f34",
"metadata": {},
"source": [
"Now, from this data shall compute the centroid of these 3 body parts. This will result in a proxy for head position.\n",
"The centroid or geometric center is the arithmetic mean position of all the points (in our case, the ears and the nose).\n",
"\n",
"First, we select the columns containing the x-and y-coordinates of all parts. We will store these in x_cols and y_cols, respectively. Then create a new DataFrame with just the x-coordinate and y-coordinates, which we call \"all_x_coords\" and \"all_y_coords\" respectively. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2f51bf19",
"metadata": {},
"outputs": [
{
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"text/plain": [
"scorer DLC_mobnet_100_unimplantedJan4shuffle1_200000 \\\n",
"bodyparts nose leftear \n",
"coords x x \n",
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"... ... ... \n",
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"154436 991.879517 422.413788 \n",
"\n",
"scorer \n",
"bodyparts rightear \n",
"coords x \n",
"0 383.521667 \n",
"1 384.808868 \n",
"2 384.428528 \n",
"3 384.233154 \n",
"4 384.890259 \n",
"... ... \n",
"154432 384.718536 \n",
"154433 384.466309 \n",
"154434 386.694214 \n",
"154435 384.249390 \n",
"154436 386.354828 \n",
"\n",
"[154437 rows x 3 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_cols = [0,2,4]\n",
"y_cols = [1,3,5]\n",
"all_x_coords = hd_data.iloc[:,x_cols]\n",
"all_y_coords = hd_data.iloc[:,y_cols]\n",
"all_x_coords"
]
},
{
"cell_type": "markdown",
"id": "55350cd0",
"metadata": {},
"source": [
"Wonderful! Now to compute the centroid. Remember, it is a mean. Therefore, we need the sum of the observations for each time point. We compute the sums for each coordinate separately, below: "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5deb543f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1037.246429\n",
"1 826.542107\n",
"2 1706.128662\n",
"3 1037.074432\n",
"4 1036.873016\n",
" ... \n",
"154432 1799.661133\n",
"154433 2367.268127\n",
"154434 1795.844269\n",
"154435 1027.267883\n",
"154436 1800.648132\n",
"Length: 154437, dtype: float64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_sum = all_x_coords.sum(axis = 1)\n",
"y_sum = all_y_coords.sum(axis = 1)\n",
"\n",
"x_sum"
]
},
{
"cell_type": "markdown",
"id": "80633a53",
"metadata": {},
"source": [
"Now, we need to divide the sum by the number of body parts. In our case this is 3, but we will express this in more general terms so that we do not hard-code our variables. Remember, it is good practice to write the most generalizable code. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c4408a51",
"metadata": {},
"outputs": [],
"source": [
"length = all_x_coords.iloc[0,:].shape[0]"
]
},
{
"cell_type": "markdown",
"id": "94ba2ab9",
"metadata": {},
"source": [
"Now, we will compute the centroid, and store it in a DataFrame called hd_centroid. From here, we will extract the X and Y positions of the head, stored in the variables x and y, respectively."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ad98bac4",
"metadata": {},
"outputs": [],
"source": [
"x_cent = x_sum/length\n",
"y_cent = y_sum/length\n",
"\n",
"hd_centroid = np.zeros((len(x_cent),2))\n",
"hd_centroid[:,0] = x_cent \n",
"hd_centroid[:,1] = y_cent\n",
"\n",
"x = hd_centroid[:,0]\n",
"y = hd_centroid[:,1]\n"
]
},
{
"cell_type": "markdown",
"id": "c13006fd",
"metadata": {},
"source": [
"Now, let us create a DataFrame for position. Time to use Pynapple! This recording was acquired at 120Hz, so we will make the timestamps first, as below: "
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "0c1f62ea",
"metadata": {},
"outputs": [],
"source": [
"fs = 120\n",
"timestamps = x_cent.index.values/fs\n",
" "
]
},
{
"cell_type": "markdown",
"id": "a3c79d4a",
"metadata": {},
"source": [
"Now, we create the position DataFrame as below: "
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5bbdb539",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time (s)</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.000000</th>\n",
" <td>345.748810</td>\n",
" <td>112.213008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.008333</th>\n",
" <td>275.514036</td>\n",
" <td>419.583694</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.016667</th>\n",
" <td>568.709554</td>\n",
" <td>274.864133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.025000</th>\n",
" <td>345.691477</td>\n",
" <td>111.544512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.033333</th>\n",
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" <td>112.194148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1286.933333</th>\n",
" <td>599.887044</td>\n",
" <td>177.650960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1286.941667</th>\n",
" <td>789.089376</td>\n",
" <td>302.231201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1286.950000</th>\n",
" <td>598.614756</td>\n",
" <td>174.674591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1286.958333</th>\n",
" <td>342.422628</td>\n",
" <td>77.413073</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1286.966667</th>\n",
" <td>600.216044</td>\n",
" <td>176.391464</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>154437 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" x y\n",
"Time (s) \n",
"0.000000 345.748810 112.213008\n",
"0.008333 275.514036 419.583694\n",
"0.016667 568.709554 274.864133\n",
"0.025000 345.691477 111.544512\n",
"0.033333 345.624339 112.194148\n",
"... ... ...\n",
"1286.933333 599.887044 177.650960\n",
"1286.941667 789.089376 302.231201\n",
"1286.950000 598.614756 174.674591\n",
"1286.958333 342.422628 77.413073\n",
"1286.966667 600.216044 176.391464\n",
"\n",
"[154437 rows x 2 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"position = np.vstack([x, y]).T\n",
"position = nap.TsdFrame(t = timestamps, d = position, columns = ['x', 'y'], time_units = 's')\n",
"\n",
"position\n"
]
},
{
"cell_type": "markdown",
"id": "873c444a",
"metadata": {},
"source": [
"This looks good, but does not give us an idea of what the data really represents. Let's plot this"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "59aba406",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.scatterplot(data = position, x = x, y = y)"
]
},
{
"cell_type": "markdown",
"id": "c30bd4c7",
"metadata": {},
"source": [
"Aha! What we said before is now evident. The position plot reveals the radial arm maze outline. There are some points outside the bounds of the maze, due to DeepLabCut detecting the mouse erroneously. But these point are irrelevant to us, since we only care about points in the centre of the maze. Now, I will define the centre point of the maze, using the coordinates (xth, yth) and a radius of the circle given by rth.\n",
"\n",
"(xth, yth) are approximate values for the centre of this maze. Feel free to pick these values as per your convenience and application. rth is also modifiable as per your application.\n",
"\n",
"We will go with the following parameter values:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "731765a3",
"metadata": {},
"outputs": [],
"source": [
"xth = 683.4\n",
"yth = 484.4\n",
"rth = 200"
]
},
{
"cell_type": "markdown",
"id": "3c26a321",
"metadata": {},
"source": [
"Let us visualize this; we will plot the position with the circle around it. And then we will only consider those trajectories that lie within the circle."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4afcc25d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.patches.Circle at 0x7ffa263e00d0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"circle1 = plt.Circle((xth, yth), rth, color='k', fill = False)\n",
"ax = sns.scatterplot(data = position, x = x, y = y)\n",
"ax.add_patch(circle1)"
]
},
{
"cell_type": "markdown",
"id": "2a80fdfe",
"metadata": {},
"source": [
"Great! So now we can restrict our position data to the centre of the maze. \n",
"\n",
"So, to do this, firstly, we will restrict our position to points within the centre. This can be done by selecting points whose distance from the centre is smaller than the radius of our circle. Time to put Pynapple to use!"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9e9bf7c1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Time (s)\n",
"0.000000 502.525107\n",
"0.008333 413.003769\n",
"0.016667 238.870630\n",
"0.025000 503.058904\n",
"0.033333 502.622715\n",
" ... \n",
"1286.933333 317.914119\n",
"1286.941667 210.607966\n",
"1286.950000 321.120486\n",
"1286.958333 530.946257\n",
"1286.966667 319.043616\n",
"Length: 154437, dtype: float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = np.sqrt((x - xth)**2 + (y - yth)**2)\n",
"dist_center = nap.Tsd(t = timestamps, d = d, time_units = 's')\n",
"\n",
"dist_center"
]
},
{
"cell_type": "markdown",
"id": "708d2218",
"metadata": {},
"source": [
"Pynapple time!"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "78c87b40",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Time (s)\n",
"0.041667 111.789845\n",
"0.050000 112.349458\n",
"0.233333 112.429611\n",
"0.250000 198.200149\n",
"0.316667 195.779983\n",
" ... \n",
"1286.775000 191.168783\n",
"1286.783333 172.081024\n",
"1286.816667 191.184213\n",
"1286.841667 190.762090\n",
"1286.916667 191.413067\n",
"Length: 84709, dtype: float64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"within_center = dist_center.threshold(rth, 'below')\n",
"ep = within_center.time_support\n",
"\n",
"within_center"
]
},
{
"cell_type": "markdown",
"id": "bacf588d",
"metadata": {},
"source": [
"As you can see, all values in within_center are now less than our threshold value (rth). \n",
"\n",
"What does the time support of this look like?"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "907437d8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" start end\n",
"0 0.037500 0.054167\n",
"1 0.229166 0.237500\n",
"2 0.245833 0.254166\n",
"3 0.312500 0.320834\n",
"4 0.470834 0.479166\n",
".. ... ...\n",
"213 1286.737500 1286.745834\n",
"214 1286.770834 1286.787500\n",
"215 1286.812500 1286.820834\n",
"216 1286.837500 1286.845834\n",
"217 1286.912500 1286.920833\n",
"\n",
"[218 rows x 2 columns]\n"
]
}
],
"source": [
"print(ep)"
]
},
{
"cell_type": "markdown",
"id": "2566a658",
"metadata": {},
"source": [
"So now we have a set of points for when the animal is within the radius of the maze. We will now seaparate these points into trials. For our purposes, we will say that trials that are only greater than 0.7s be considered. Additionally, each trial can have a maximal duration of 20s. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "51fea121",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th>0</th>\n",
" <td>3.712500</td>\n",
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" <td>12.562500</td>\n",
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" <th>4</th>\n",
" <td>22.937500</td>\n",
" <td>23.829166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
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" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1037.437500</td>\n",
" <td>1040.520834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1048.729166</td>\n",
" <td>1051.954167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1075.654166</td>\n",
" <td>1078.345834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1089.045833</td>\n",
" <td>1091.445834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1093.912500</td>\n",
" <td>1275.204167</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>76 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" start end\n",
"0 3.712500 4.804166\n",
"1 6.695834 7.912500\n",
"2 9.329166 12.562500\n",
"3 17.812500 18.612500\n",
"4 22.937500 23.829166\n",
".. ... ...\n",
"71 1037.437500 1040.520834\n",
"72 1048.729166 1051.954167\n",
"73 1075.654166 1078.345834\n",
"74 1089.045833 1091.445834\n",
"75 1093.912500 1275.204167\n",
"\n",
"[76 rows x 2 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ep = ep.drop_short_intervals(0.7, time_units = 's')\n",
"\n",
"ep"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "336a76b6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <th></th>\n",
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" <th>end</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.712500</td>\n",
" <td>4.804166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6.695834</td>\n",
" <td>7.912500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>9.329166</td>\n",
" <td>12.562500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17.812500</td>\n",
" <td>18.612500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>22.937500</td>\n",
" <td>23.829166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1034.495834</td>\n",
" <td>1035.462500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1037.437500</td>\n",
" <td>1040.520834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1048.729166</td>\n",
" <td>1051.954167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1075.654166</td>\n",
" <td>1078.345834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1089.045833</td>\n",
" <td>1091.445834</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>71 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" start end\n",
"0 3.712500 4.804166\n",
"1 6.695834 7.912500\n",
"2 9.329166 12.562500\n",
"3 17.812500 18.612500\n",
"4 22.937500 23.829166\n",
".. ... ...\n",
"66 1034.495834 1035.462500\n",
"67 1037.437500 1040.520834\n",
"68 1048.729166 1051.954167\n",
"69 1075.654166 1078.345834\n",
"70 1089.045833 1091.445834\n",
"\n",
"[71 rows x 2 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ep = ep.drop_long_intervals(20, time_units = 's')\n",
"ep = ep.reset_index(drop=True)\n",
"ep"
]
},
{
"cell_type": "markdown",
"id": "c16d1d5e",
"metadata": {},
"source": [
"Now, we will plot the trajectories corresponding to our trials: "
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "61819f01",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7ffa241f16d0>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"sns.scatterplot(data = position, x = x, y = y)\n",
"plt.scatter(position['x'].restrict(ep), position['y'].restrict(ep), zorder = 2, label = 'selected data')\n",
"plt.legend(loc = 'upper right')"
]
},
{
"cell_type": "markdown",
"id": "436cd2b5",
"metadata": {},
"source": [
"And voila! We have now obtained the trajectory of the animal within the circle of interest!\n",
"\n",
"We can also go one step ahead and consider only those trials where the animal goes from the departure arm to any arm \"in front\" of it. We call these \"forward trials\". To determine what constitutes a forward trial, we use the following logic: \n",
"\n",
"1. Any trajectory where the y-position at the end of the trial is larger than the y-position at the start of the trial.\n",
"\n",
"2. Any trajectory where the y-position at the end of the trial is larger than the radius of our circle. \n",
"\n",
"So, first we define the change in y-position as a Pynapple Tsd: "
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "ee969447",
"metadata": {},
"outputs": [],
"source": [
"dy = nap.Tsd(t = timestamps, d = y-yth, time_units = 's') \n"
]
},
{
"cell_type": "markdown",
"id": "9bf4f989",
"metadata": {},
"source": [
"Now, we will compute the variable diffy using the 2 conditions mentioned above."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "0d4c7dbf",
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>0</th>\n",
" <td>3.712500</td>\n",
" <td>4.804166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>17.812500</td>\n",
" <td>18.612500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>45.120834</td>\n",
" <td>46.187500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>54.229167</td>\n",
" <td>54.954166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>78.929167</td>\n",
" <td>79.795834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>119.229166</td>\n",
" <td>120.279166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>151.295834</td>\n",
" <td>152.462500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>193.704166</td>\n",
" <td>194.629166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>220.104166</td>\n",
" <td>221.595834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>252.279166</td>\n",
" <td>253.362500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>263.329166</td>\n",
" <td>264.287500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>290.395834</td>\n",
" <td>291.920834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>298.479166</td>\n",
" <td>299.445834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>328.287500</td>\n",
" <td>329.429166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>366.204166</td>\n",
" <td>367.270834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>433.812500</td>\n",
" <td>434.770834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>464.170834</td>\n",
" <td>465.137500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>542.204166</td>\n",
" <td>543.304166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>640.045834</td>\n",
" <td>653.904166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>674.945834</td>\n",
" <td>682.537500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>746.012500</td>\n",
" <td>747.095834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>988.612500</td>\n",
" <td>989.595834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1019.379166</td>\n",
" <td>1020.329166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>1034.495834</td>\n",
" <td>1035.462500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>1075.654166</td>\n",
" <td>1078.345834</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" start end\n",
"0 3.712500 4.804166\n",
"1 17.812500 18.612500\n",
"2 45.120834 46.187500\n",
"3 54.229167 54.954166\n",
"4 78.929167 79.795834\n",
"5 119.229166 120.279166\n",
"6 151.295834 152.462500\n",
"7 193.704166 194.629166\n",
"8 220.104166 221.595834\n",
"9 252.279166 253.362500\n",
"10 263.329166 264.287500\n",
"11 290.395834 291.920834\n",
"12 298.479166 299.445834\n",
"13 328.287500 329.429166\n",
"14 366.204166 367.270834\n",
"15 433.812500 434.770834\n",
"16 464.170834 465.137500\n",
"17 542.204166 543.304166\n",
"18 640.045834 653.904166\n",
"19 674.945834 682.537500\n",
"20 746.012500 747.095834\n",
"21 988.612500 989.595834\n",
"22 1019.379166 1020.329166\n",
"23 1034.495834 1035.462500\n",
"24 1075.654166 1078.345834"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diffy = []\n",
"for i in ep.index.values:\n",
" tmp = dy.restrict(ep.loc[[i]])\n",
" diffy.append(tmp.iloc[-1] - tmp.iloc[0])\n",
"diffy = pd.Series(data = diffy)\n",
"diffy2 = diffy[diffy > rth/2]\n",
" \n",
"ep_fwd = ep.loc[diffy2.index]\n",
"\n",
"ep_fwd"
]
},
{
"cell_type": "markdown",
"id": "d515d8a8",
"metadata": {},
"source": [
"We are left with a subset of epochs that are forward trials. Let us now visualize this: "
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "fee18369",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7ffa240ffd90>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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0uIK1Kxbhxoe26sf0PIGK9uFLZR/b0s7tvLrzxvhXAyMX8tuWxn+yJqN9/K3YB3wZGgyY0sQPOLJQSYB+m6p8fH5MQHwQ5pHtw3FgSm8AxQciL2Nse4237W0T9bNqDNU6wz1Pz7zGj8kI50n7AMBbTlmGzTsP4pEte3Pd7lyEmfZJr5CF9ulx/i0iOdU3ea/81uRF+wBm3h/I5vkD+lRUG6eszlg4ONWcf5z20XP+/u+ZDvhy2LyYtu7JL+NHvManvWIBhsrerPD+1Xp7jT/fOgWm6IITj0DRc+Z14JdfC3PAN73MmvZh/p7agflj/DMGfDkmNbRPVi/dlB8PNJPr35omXj0W8JWfaxjwbXDP33ze+y0C3Bx5BHyja6zYr7DDdgR9iQie6+B1q8Zwx5M7ZjygJ5uhAtF1ySuozjFULuC81WP43iMvtiWGMhfgBNbTyPlLltlfMoZTj1lqXq0JzBvjX+VX28J7FSFbLyzyMlAbSdhk8th7/mpv3YaH5avULIq8nFSqpwXtM0uev82x5Z3uKeK81WPYeWAaj26dWTpEFZvis7rWOf/0mLr4lGXYeWAa9zzTXQ10bCW6w1iYifaRpXr2OP+ZQy1xh1RSBhy6VE+OLBktAHBQU+WbNY7AkYfnHwWILfP8LWIJWQK+eXqlM1/oFR9Xr3vVKBwCbpth6mdK4flz8GK6liEwEOesGsVQ2eu6rB/bHggRHWr2/L/5zW/i9NNPx5vXvxZ//fE/QbVWw8DAAD75yU/ipJNOwtq1a7F9u58ptnnzZpx55pk48cQT8bnP/lUu5yQ9/rZtucOQzPaxTb3MhfYJftt4pVlpHx3nD6gHJl+aJc+fr1qts2gmpYDJ8+fHVXQdvHyoajReJvCXjmp2xT9f1F9si+fPbeKC/iJOPWoB7phx46/w/IXbn3fQt+S5eNOrx3HLppesqMBOR9YmQ0nNKxWefvJX+Nd//Vfce++9+O7tP4XruPj2v34LBw8exNq1a/Hwww9j3bp1+OpXvwoA+OhHP4oPfOADePTRRzG25IhczzF2/G3bcochyvYxe68iZBIKUZFXNVOqm472CTn/w9niCNKXk3BIpuyXuvBStKV9APXLM7w2lp7/6KCvD5OXYTK9YJeOlLEtR89fdvvPXT2GR7fuzVW3yAQl7SMa/wOtX+OkisHFJ4+HSp9zHVwmu+yWAZglusmS9vnp3XfigQcewGte8xr8h/Wvxc/vvRu/fn4zisUi3vzmNwMATjvtNDz//PMAgHvvvReXXearDr/l7bJWKfmg640/H6wp2qeFVE8OLmBmhCkYKSBrHMFk7FQZRvylVW/Es3hkL7NkwNdmv0bPP/i9ZMg3/q2me9oWoI0PV7D74HQ+FIgA0Sied6xf7XvHkzPn/etmTlzoL2/PHwBec/RCLBup4MYuyPqJNQaSNDZKgt9ypuTvA4eTMbz73e/GQw89hO/e/lN8565f4CP/9c9RKBTCF4jruqjVomfGRhG3VXS98fcCy1VtZE315AFVvXHPQtPoOf9ge5aeP4e0DgFmD51D9PyZIk+eEqme/nZVLxX/ty3nv2TI97LyCvqq7is/rvERXwWxnUHfVUsGMT5cxm1PzKTxVwd8x3J4waomuI5D+N2Tx3H30zuxM4eZxWxj9+RuvH3V2/Evb/wX6yZDJtpn7dnn4Nvf/jZ27PDHw96XX8YLv/61cv2zzjoL1157LQDgxuv/NcPRZ0PXG38uTcA9f5tcdREmTj2TrLNNto/ly8Q2jmAy/rUGi70sZOebrPAFzNfP1viPDXLDlA9FYrrGywLjnxf1I3vsiQjrjx3DPc/sbDmWYQsd5794oASifDx/mT968cnLUG8wfP+RbS1vf7ax4dwNuHLtlVi1cJWyCx4Hv/emxJ1jVq3Cpz/9abz+9a/Hm89Zi/e96y14aftLyvW/8IUv4Itf/CJOPPFEvLStfcH0XIw/EX2diHYQ0WPCsoVE9GMiejr4vSBYTkR0FRE9Q0SPENGpeRyDCl6QjJsq8mrB+IuwMf5hMFLn+Qfr5FLkJQxGdQDURzLlTHZdZIEt00vHdF34phYNlOA61LLnz49MJe6W9Py35uz5U8Isrl89hkPTdfz8ud257kcFlbw34M9+F/UX20L7AMCqI+a30qdNkdc73vEOPPTQQ/jenT/DtT+4EyeesgYHDhwI17n00ktDWecVK1bgvvvuw6OPPoo//fNP4aHn2vNSzcvzvwbABYllHwdwG2NsJYDbgv8B4EIAK4OfKwDo86lahOcGnn/CyFlz/gbaJ0uVr80LJ2uqp+n4TE1kbATveAcoq4BvmOppd11ch7B4oJib5296KR0x7HvB23Kq8lU99689ZjHKBWfGqn3VtI+PxQOllow/v68qLno+K32acvZlnzYadvU47UQuxp8xdjeApItzEYBvBH9/A8DFwvL/x3z8DMAIEbWnhA0R559M9TRy/jybRqPtA1h6/sHqVvIOlhlEfB1pEZoFNx+qCwp5/qrtUSLV09+u/vpNVhvadFCRahobLGN7Tpy/6biKrovFA6XcOf+kTSwXXLz2mMW4/Vc72vqQc1pTmXjA/GMbHSxhIucqXxFc6fPGh+af92/U9pHcf673ZcJcLfJawhjj85WXACwJ/l4G4AVhvS3BshSI6Aoi2khEGycmJpo6CE77JIXdrPP8jfRGPrLOfHxU68zozYvQVSCb9gmkvRbZvmWpnoer+oAvYPeyA/yMn7yyfZTHFfwmAsaHy3gxJ3E3mWgXx/rVY/jN7kN4dqJ93nDJ88e3LrZAIIwNllvz/Bnflhxc6fOmh16cdY82iXYfj0nVMz5Eon+sJUAsEn+aOccZCfgy/8gyHx1j7CuMsTWMsTWjo6NN7TsZ8OWwV/U00T72NI2tMczyQjHRVyZJCRs6LOT8hXXz1PQfHSzn5pXq4ioc4yOVXHP9AfnzeW7Y4KV9OfCh8TfIO4wOljBxYKqthrATlT7L5TJ27drV1vM2cf4qC2Ir7may/Ywx7Nq1C+Vy2Wp7HO3s4budiJYyxrYFtA4nP7cCOFJYb3mwrC0ouHLax57zN5TNWwRobWYbjPnVrtP1BvZNVjE2pL+RfJsmbR9l6mMo6WymwyLPX7+eeFyAPuNHHPdjgyXsPDCNar2BgtuaP2LzUlo6XMFdT00Ye6+2imUjFaw+YhC3/2oHrlh3TFv2UQw9f3W2D6d9qnWGPYeqWJBDL18ZLjjxCFx502O44cGtOOnIkbbsIyuWL1+OLVu2oFnmQIcd+6cwXWvAdQjs5fTz2mAM2/dMggB4+yrBdyZRrflPH3u5FN4/FXYemEKDAdVdJe165XIZy5cvz3T87TT+3wHwbgD/I/h9k7D8w0R0LYAzAOwV6KHcwT3/kPax0KcRMRkUPsmMBFG2hi4mL3yoUsDOA1PYmyHX30QRmTxhG7VTkqV65tTQhQhhHvrOA1NYOlwxfkcOfl/1RW0AMD5SxqHpOvYdrmG4r9Dk/vh29Z+vXz2Gf7z7Oew9XMVwpbV9yVC0oX1IqKQ+MNWU8RdpMxVEpc8r33QsvBZf5HmgUChgxYoVbdn2J754Lx56YQ8GSh4e+6s3pD7fe6iKN/31jwAAz372jXAdwn+7+h78Zvch7DlUxf99z2tw7qox7T5+/2s/x4GpGm744Mm5H39eqZ7fAnAfgFVEtIWI3gvf6P8OET0N4PzgfwD4AYDnADwD4KsAPpjHMajAPcmsev4cDabWSh8oeZYBX2a1z6GK/y7OUjgm22aM8zdw8zYBX1mqp65RDK8otVX2XDKYX6GXjeffjnRPlVE879gx1BsMP3k6f88TiFJMTdk+YznLaKjQrUqfOhw0delDpCrMmP+SBOyTRdo1N83F82eMqQQozpOsywB8KI/92iD0/C0CmypM1urS6dlQuZCJnzd6/hkHBWDRxD2j568r8mIWtA8YMFD2cLhat9L3IVDo+efRzlF1vuJZjoeFXodx3PhQS/szef4nH7kAC/oKuP2JHXjzq8db2pd0/8GZqbV9GAgUev7NptRGAV+9KRKVPs8xeLXdAl4ZXw6cnnC5MOrE52ywHDh5VpRx+6jJ2Z+XtRlhqid/8wbLTUZTfJMnDSL/aLBs5/lz6AwxYwxDAS2QrY9vk0VewTrJjAM55+//FgNUukrawZI/uG09/zHu+eeQjaKa6XDwbB8gb4kH+QPqOoRzVo3hzqcmrDXcm4E17dNmz7/blD51yJJVV61x+8MyO3lOm1z/7jf+Ie1jNnIqqLyqoXLBSogtqj7V32zOCWehfUwBadN5JuUddJy/bZEX92x0hV7iC2rxQBFE+fTytbmviwdKKLiUs66/GueuHsPug9N46IU9uW/b1MuZX+bBkodywWm78Qe6S+nTFqZMPlFbrFJ04TlkxRo0gplbO9D1xt9VCbtV69Z5tioDm9Xzn6w2lN4fA1D2HBRcyhZHkB6b/1nJc5RyBxx28g4ZaB/4g5vIzvMn8l/Qi/pbK0IyZVSJx+44hCOGy7l4/ro8f47XrRyF61BbNP6Nxp/TNURBoVdzxj+q8DWv201Kn7YwFVuKzifBtx02Th5jaBvp3/XG31Pk+QN6rXvxxiXjA/xBGKoUrKdufUWfD9RNhYn82UQ22id9DvzYB0oeDqkknYPfKc5fYjz5A28j7OZPU8kPhutSPRNGc2ywlEuVr+mlzr2opcOV3CQeAL1RHO4r4LRXLGhrdy+dtg+fuY0OtF5MZ4NuU/pUQjASRs9fCPgCGWwH2hfw7Xrj7yY5f0Nmiwyq9XzP306Ooa/oUyEmyeGhSiFT+qjuHPpKrpWks+014bRP0XWM2x0seXaef/Dbr/LNp5G7rgcDx7KRSi5Vvra1Q+etHsMT2/ZhW06VxUmYaB8ArXn+GcMV3aT0qQPPbDPRjWLGIFEG1oBFM++80fXGnyPp4QKtK3sOlj3rhi79JX+Q6GgYAvnTwQyFYzrJ6f6ip9Hd99dKN7kxz4YqRVc72In8jB/bIi/AD/q2kurJjPGIOG2xdLiMl/ZO5haENT2e68Nq33y9f37eyoAvY+GxjQ2Wc+nmZYP5ovQZzej1KddJz3+wZJcp2GDMimprBvPb+CdumMqDV2X78Ki9DXfXH3j+pulh1vRRXcC3r+jmwvmLNQEA0F901fUDwe+Bkt74c/CBPTZUws4DUy0ZY/4g2gR9x0cqqDXYjNESvzU2gCMXVnLn/fnV0jkg/BqPDpawJ4d+ybbodqVPBn92DVjIqCScrKGKZ9W4icEuztIM5o3xryZSPQG94Yytp3iwBjOkbHHPX5clQxQMCsvpIOBPJ5MFbNxY95c8oyGsM2Z9TTjtY/L8AWCgbMdpcowNltBgwK4WjLHOC0tifMRP98xb118FIsL6VX6Dl7xbSAI6bZ8IPN1z54HppveTxRDNB6XPfgOdyxFPOCEMWjp5jLEe7dMsuCEU37w8DpD0ckXHPx7wVdM+gN7489kE5/xtCr0yN3RRvJz6i56y+pAvSXn+0umrv05DOBd1Vo2fmjZo8PyTR8S1jJoNSDLor3GU9eL/Dgu9cgr62hTirD92CSarDdz3nLk1oC2ibB91LCmifWYm15+jk5U+80A8kUOvsRXl+fuw5fzbWBrS/cafQxR267MI0og57clMCv4JL8qyeYOHnr+qArWJwrFiUMOQoq+CI+wruX4AVMPj1+osHKSuQ4aAr/+7UnSVzd45BqwDvr5p4oaplSrfLLQP1xBqNd0zi007Y8VCVAoubs+xt29Y4avp4cs9x1YKvfi9zppz3olKn3mCOxymgjaRdvYDvgXsn6pZNYLpVfi2iDDazhjK3EgojDoQN/4q42nj+XPYDBKe6nm4Wse0IYjMAJQLTnB88hlMv2afSR4f8DMXdJx/5Pm7ymbvnKM0B3wTqZ4tev6AT3MB5uAbAAyVPQyUvNx0/W0ez3LBxdkr29PgRWX8Gw0h1TPnfsk2uODEI1D0nK4N/BZcgueQkWqcTlCzQ2EhpMF2CAH7vNH1xp97RiIvbpOeJb6pVbTPUGj8zZWsAxrD5B9nsM0Ms4lK8BJTHV+fIc4AxGdE5YKrbV4TBXz15wJEAV9TIV0YjBwIDFOTGT/WtRTBo0REWJpDoZdNkZeI9avHsHXPYTy1/YB5ZZv9c9pHMQbEbJHFA63TPlmdUFHpMxmbmuvgujuVojylWhwZtYSqcCTxoH/OGXryDi1DFHbrU3n+gjfGDR1ROm88me1j5/nbZAVQJPpksc3wJaaYweiNtL9WvRGZr76i3PMPvyHQPv52Nfo+ZbsYB0fRc7Cwv4jtLXiltjnXHEtzbOpiaxS5hG9eKZ822T7ceBRc/xo3R/s0cXABulnpk+A/NybaR2xpyit8ARgzfhpt7Dkxb4y/KOxWtjAS3PiXPVdJ+3CawcZQlzwXDpk5f2uPgLHwPEzHpzPAolibkvbh6zYi2gdQBIcTMx3VtFZmS8YGSy14/kw4X/Vxic/RspEyXsyxytcGRwyXcfz4UG7dvUR5BxmV1Ehki8xUla8IUemzG9GvSIAQb0eyn7XtDL+dks5db/xl2T5Kz1/4mxu6isYbdp1AxkBH+wS/iaLsGxX8VE+u7Gnh+RtmMP1FfZAZAOrCdSkX/RediqoRs30AA+1TzqbsCfi8fyv6PtFLyW6fS4cr2HlgqqW892Y84vNWj+GBX7+MPYeaT7mUIckrA+mx0WyVbziOmziublX6FGfCJs4/medvGy/kndjaga43/hwi7VP0HDikDpQCgvEvuOn1hNdEluycvpKrMcQBF5ihoYuJ5ugrmQO+NaGZC8+CSlEIjMW+Y6pqpEDbB4BS30dmNMcGW/NKC65jFXzj4OmeL+VA/WR5QM9dPYYGA+56Ko8GL+rEBMYYXth9GD987KVw2VgLEg+toFuVPrlTl4n2IaFGyKAK3Mv2aQGR5x/n3CoFfaESp0NKBUdZQANE+j7q/fMUuWCQ6AS4IBaOmQdFJTTW8m32WxQ9xbJ9DAFkfk0qBf16QOTZmDx/cWBzw2SrtiqCf0MdfIvuAwfX9W+l0KsZKvyk5SNY1F/MhfcXX6LJcSC7jNzzbzbbqFlDxJU+uzHrR1X0KDqJyWZSQ5acP+tl+7SOKNru/y+jc8Sb1Yhx/jojZ1/J6nv+piIvu0EBIEpZVXn+uqKn4Lef1RQ36io6LKR9dFlEwWAdKPkvMRuJB44lQ2XUGgy7m6RDiOyCbxx5FnplyX93eIOXJydyyYAp8T6+CSelITHwo4MlTNcbVuNLRKuZqVzp8yddpPQpzoRNMiphkVdYz5OB8+/RPq1BTGkkIpQVwU2OMOBbcJTZPoA97eMbJjXnL+bmO5SN9lHm+ZfsA9uAOoaQ3K5NSqWR81fQPkBrvXxtgm8cR7Slo5cdzjt2DHsPV/Fgiw1eGATjn6DrZAVEUSP3mQ10A92q9EnoK5plVGL2B4Si56DkOWbOH3O4mQsRPU9EjxLRQ0S0MVi2kIh+TERPB78XtGv/YZ5/4kEw0j5CwFeVTRNW6lkEfAHulerVMJ0giGySeGBM9NTl/QZCz18X8E0UeQEywbv4dyqFQM9E8ZIggpHzD9cV/g57+TYT9BVndIZrzFEuuFjUX2ypo1ez9MnZKxfDcwi3tVjtK2Z9JWkf2aFFhV7ZXrAy2iwrulXpUznbjGX7pG/GYLlgdPIYA5w2WemZ8vzPZYydzBhbE/z/cQC3McZWArgt+L+t4BefD2Ip7SMGfJkd7TOUIeDbX/SUqp7i0LBt9MA9PpUR9hxCyXOMFb7J/H2T8JhOpI6fx4Chj6+sOIr38p1o0vMnkPEFm8T4SCUfz7+J4qfTVyzMReWzrAjU8zF8/rFRI/WZ1vdJopuUPv1grCXtE6aaR+PeRsSxG9s4XgTgG8Hf3wBwcbt2JAv4Av4DY1PhW5Zk+4gwcf5RfrnZMPGbPGThEQD+LKFccNLaQ4Jd7S+pRdiAdIUvoC9+E9fTbdd1/PPV9fEF4p54HvIDFUXmhco/XzpcbqnBSitU+PrVY3hy+35seflQS/s3cf5rX7koXDY6ELxgmzT+rfLP3aj02Vf0MF1raOM3tUQzF8AuXsiAOd3GkQH4ERE9QERXBMuWMMY48fcSgCWyLxLRFUS0kYg2Tky0lhYXE1aC/7bWyhc3omwfJe0TVORO1xtWMr06QywaWButb+5B6GIXkVciM4b+9xtMEGyzrJB1KIiFKHSA+Fi11fTnKBdcDFcKTbVz5F5Yv9Hzjz9J4yP5tHNs5vnkDV5a8f4ZQybaZ6jioehlb+SelxRRNyl9Roq9gTOkqRtKFnkBnDUwOHlzvMjrbMbYqQAuBPAhIlonfsj8KygdBYyxrzDG1jDG1oyOjrZ0EMlOOqpqVg7R89cVAQ1lEnfTZ6KIHoGN5w/IYxfiM9VX1NUW+ODev6r4TXKkgayz/pwHNJSY6rn3c/1b8fyz0j5l7J+qWV/vJFqxX68cHcDRi/pa6u3rc/7ygC9/qsT0TCKalSpfEVzp8+EuUPokRNlvOqepmsg2BOzk2xnmcBtHxtjW4PcOADcAOB3AdiJaCgDB77Z1tg5TGhMBF5PxbwhFXrqOX6aULZHj6y/5bR9lip0xzt8yfZTXK6j0/IkbaY1SJxAvaAMk2UOSbVcKaiPLjY1J05+fg4glQ+WmDJPohcmVSYOAZWKHeev6Z8X61Uvw02d3tVT5quL8VUHaZqp8+RjIo+CIK33e2CWBX5smQvFsQ/+3TabgnG3jSET9RDTI/wbwegCPAfgOgHcHq70bwE3tPA4gffHLRReHp+UdsAAh4FtwlMYVyCbrXCnoUyT5PfZpH3MWAACUZC8n4W/f89cfW5jWmkEPv8+isGWgrNb0VznMrej7+HSeeUYiIi9d/2aN4vrVY5iuNfDTZ5pr8BLn/OUzwOShzVaVL0e3KX3y7LdkModO2wewM/5zWdtnCYB7iOhhAPcD+D5j7GYA/wPA7xDR0wDOD/5vK5KpVjLZBhGisFu9waQ3TyzTVtEG4gNo08QdsG/04J+HI+F6Iy/Xry2w0x3pU6WOJg4jKqZSeP7BbxvOP2k0R4daq0D172vDuhfwssDzb17XvzXe+vQVC9FfdHH7k01OfpnvAAAyz9+H1POf5UKrblH6FJ9rHZMQZRtGGAx6d8hsCweXjW4HvLZsNQBj7DkAJ0mW7wJwXjv3He3M/1WTcP6HghaHsotbb/jTrbJAhRSCzlnJIi8gY0MXiUGMc4GRIuZwIPQmBZmzlvpLrlToTByENSG4DZhTPQFNSbuw4YFStj6+ALBksIzpegN7DlWxoL9o/T2+W/FB5OmmImSG0HOodc+/ye8VPQe/vXIUdwQNXpp50MuewviHTkDiBTtYwu6D05iuNVD0LP2/nIOzotLnOavGzF/oYKhon5i8Qz1e5AXE44ULFWO9V+GbA+LaGn4DhgaLKyGKN0us8AVsunkZUhpBxibu/CGNlD3NxWOy2EWS9jF6/gEl5hBJs3hkj31f0TMGhgc13bxUnj0v9GqG9yfyUz0Bc1s9DtchLBkqN83552ET1x87hm17J/HEtv3Z9w8IAV+5tk+yGQivp9h1cPa8/25Q+owcSbVTx5GMOQJ2Eg8+7TNHA76zDVknL0Dw6KflRr0eesPqwqe4EJu95y9vMC6kehqoJBFlTcoqp33kHnr6RRcK3hmMOl9PVTzGPRVO++gonDQf7RumZnv5qvoz64z0+Ei5JXG3VnHOKj+TrRmNf8YYStzzV1R6Jy9yM718eSptnugGpU8bpw6InMx4sohZx4vN1YBvJ6HB/AwefulNaY1iqicQ96pEOzKQoaFL2FnLkHppmz5KIHmzGbHIK8jz1xlg0SsxZTeF6xlkFAA/4FtvMOmsSRfwBZqQHwg2GHZMU1xjGa2ydLj1jl6tPKBjg2WctHy4KZVPBsB1fPpImeqZ+M5oDhpKeaBblD4rqjx/YZDHnM/ghtg0dPFTPXM5zBTmjfEHgGpAbxDJFSzFm9UI1CnLnp72MTV0EQO+fJDoVDYBO9on0rJxNEVehErRA2Pq4wcEfR+eBWXy/Hm1snI9f7RG+j7q80iO64j2yW6MiSjsYXC4ak8ljI9U8NLeyZakpFvFuavH8OALe7ArYyCWt/kreZLAf/A7+WKKxN2y7StvGzTXlT6TGlpa2kcytmxatnajvMOMQZbPDphbOdYTnr+U9uH57Jb6PmbO3/8d0T6mTBlzs5lon3YD05QFFa5n0bTaVtNfRF/Rw2DJa9ortQm+JTE+4geZd7bAgbf6gJ63egkYy97ghdNsJc+VBHzlx7Z4wA8uZqJ98o33huBKn997eG62eBQdSS3twyWdhWU2LVt7Ad+cUK2zkMIwyRenjb8QGE48CKaGLhyRyqbeGEZcoAXnH3D0MmqG570DpoYucX0fc4Uv0Ffw9UxkKZUi5w/INf11xmR0KHuVry99a34QZc/R+HDzhV55GcXjx4cwOljKXO3LmB+oL3nppkNhkVfipEuei5G+QuZr3I6UQ670eeMc7u/rBhpbSQdLllEHCPU8Fk5eO2ItHF1v/GM3QODdZNWs4rp+qieFmRQ6g6gTaBKrLHWNz8Wd26SPito+LJm1JGyrT0E1xQtQ+DGSUeo6eS6pAS9s2KTs6W8sPbKXDJZz8PwzFHqNtK7r3+oD6jiE9avGcPdTE9q87yQ4PVmS1nsExyb53uhAtkIv3cypVcxVpU/RK1f1keCQ3dMBi0xBfx892qdl1BJZLYDaQ7RpWchviS3tU3AdFD1HmXrJp+ee66C/6BqzfQjyrCWR67UpPU/SYaYiL0CoBta8FMPBLfP8NcZkbKjUnKY/1DMdVbUrIBZ6zY7EA8e5q8ewf7KGjc+/bP0dPyBIcton+C0757Gh2a3yFdENSp+mBIiwyFQY9q5D6C+6BlXgXhvHXCC+fSvFtEcveq0NDeefNFy6hi5Jo6MSd0uawqGKWfQJULdeBCJtHyCdYSSeQ4zzN6idcqhSKv39+hjkrRw1g1s2sLnEQ5YqX8b8jdmIbCUxXCmgUnCb8vzzVKY8e+ViFF0Hd2So9m0wBocQBHwVRV6Sqzw6kL3Kt11GaK4qfabradSz4FgPceFtPGgQd+vRPi0gfgPSAd9JhZFIVb0a9H2yNXTRB3xtthk1YElX5drQPiLqsSwoR6IVFH8gbWcUA2U156/DkqEypmoNq/TZJPoUMzqdTSEiLB1pTdc/j+dzoOThjFcuxG1P2Oe9s8A6+Jy/4pwlBzea8QXbbps8V5U++Yu1rwnaB/B1vMyef4/2aRk1wchxj1gX8BVpleSDxbcDZDP+Ss8/8XTZNHQhikr7pZ4/+UqigN4TTmX7aCSsw/U0xj/kQQMvXGr8dQHfMA/dnobhm/NcB0XX0QR85Q/SspEKtjYT8M38DT3Wrx7DsxMH8etdZv6bjxmH/GLEVJ5/APnsyn/BmtpsxrbTLtcfc1/pUyZ0KIurJceLr+PVy/NvO6qJYiYgQfsI64rCboBe72aoXFA2dImcr8BDKKmF1sR7PFTRG3++Xc69q2gpm4BvbEZkGMTRduUvFXHdkueiaGhSLeWjgyrfrIVe/BpXDH0TZFg6XMa2Vqp8c3pAeYMXm4KvSL6BFLRPcGiSi5y1yrfdZMxcVPoUHTadjIrnUELbJ8JgWd+4qdFon7Bb1xv/eLZP1K827H+ruGG8BV7BJThkTvUE7CQe+otmoTW+TVM3L8DE+Qv0jKaqOBkIn6o1tAVPvFcuIM+qEb1rX9M//RLTGZNWCr0AeTcvk/EaH6lg4sCUtNeCDnnTIa9Y1I9jRvstjX903+RFXtHnSXRKla+IOan0GcbyPOlzDQCeS9IiL4D37lA7ec1Qn7boeuMvIqzwBYX9b1UefWgQiZR9fMUiL0CespVsItJnyfmbBgU/D2lAOsb567NfgHief5jdpJCz4FD2+02srdP05+eQxJKhwPPPYpiE3eoyL1RO1PhwBYw1rymUJ9avHsPPn9ttrAfh99BxgmwfRZaW7JybqfJtF/fMISp9zjXoPP+C66DKi7wS3oKOMn5sqx//uOanz+d3oAK63vgz5qdUAeZuXslqYP7QlA08OM9qyaOVI8dQxcO+SbUmT1isJitC43+Qf+4lL12AIiLM8yeKit90jebJULMgIGsfX/6dvqKbuZev+ILNSvvwjl7N5vrnaRjXr16C6XrD6AE3BMfCz/PPkOqZlfaZgSScuab0KV4SncNRdJ3Q8QSSiR1+jZDsOW+32GDXG3/A59wAP91K9Ex1rQhF2qPsOXLjGiAT7VOSewjJez9ULqDeYMZ+tGERmjTlkoJ96jMRpLIXipcih1JGgcUHt697lK3CF2itl29F4oWZslrCQq+MGT/tKH5ac/QCDJY93P6EnvoR5RuktI8m1XO4UkDBpWzXuL2OP4C5p/TJL0l/4HDIxlnBdULHM53S7WG63pAG60u2vRaaxLwy/tUE76YTMaulCp/0Fb6AgvZJ/N9X9JQCUMn8X3+b+mCplPNPDEBpDrLwd53FX4iAuaGLSR6DQ6fpD6hpmLGMvXy5vAPA4yr2ef5AJPHwYpO6/nnG5Aqug3WvGsUdT+7Qxl74i8dRaftojo03crcP+M5M/v1cVfrk/UHEe8Afq4Lnc/5MiNFw6DoBWjfaaRJdb/wZgEJwEcV8diBIaxSNhDC+RTW9csGVpnpyZA34HpJo8SQfrqEKV/xTNYb3Icv24YgVlukCvvXoukSCd/rAZ9F14DokD/gmPH+pto/BmPiFXs1X+WalDSpFFwv6CtlpnzbZxPWrxrBj/xQ2vbhPuU4y22e61oiNK122DwCMDpU7psqXY04pfbLo2vZrUp8Ljm9/kq1kAb18e8/zzwGecPGTBVAmYTcgaOIuBtMkOfmA3FAnH8C+klpiOZbqWbaQdYY8FVU620gVACkqfKUpsOlBS0Tok9BmyTXNAV85xgazef4iZPyrzgvmyEPXPy+cs2oURPqUzyTnDyQ9T3W2D5Bd32cGWB8Ac1Pps0/TQY63f63WG8pMQdlzzpv0tAuzZvyJ6AIiepKIniGij7dzXwVXHvBN0jmikRNn27Jsn5h3m9HzB/R594Co9a3Xwi+4BNch7cukv+Rat5iTyV6ooOoiJnLMA6VCpkIijiVDJRyarlsHi8VYg03rShnGRyotBHzzxaKBEk4+ckTb3Ut0LGTdvEwvvNHBDMZ/ZlgfAHNT6VNW9MjtScGL2x9xJjakoXd5ospf/ofj2nDEs2T8icgF8EUAFwI4DsBlRNSeM2QsfPPWJLSPDTdsyvaxEWjiqOi6eYmpnhU95y8++GXP0QZoK4V0YZmsoE2saBavS3J7cZ0ifWB1sOxLPysVJ5Wcv5+N0kzqpTTzwsJ4jY+UMxv/dtrE81aP4eEte5UGOlbh66X7+Kr0/DlGB0vYfWjaWkW0nRW+ScwFpU+GuIMFKGgf7vk30tfZJrZ3xHC5tQNVYLY8/9MBPMMYe44xNg3gWgAXtWtnXuD5Jzk3o4hZmOqpz/YBNOJu4QPoQ+X5J2FL+wCBsdPUIfSX5IVlHDVZnr9NQxdNthQHl3VW1TaoEFb5WqZ7ivekv+hnUMgqRXXVkuMjFeybrGVOTTVtt1mcG1T7qoTehAZsgvEXx2m8xiSJscESGAN2H5w2HssMOv4A5p7SZ9jE3UT7pEQh1TP8dqfXzpbxXwbgBeH/LcGyGIjoCiLaSEQbJyaydTjiYPCDkwBP9Yygy/MX4ffJTdA+iXVs9X14m0FZ44dYZaxFizf+VCdpqaT3baJB6glVT0Bv/CMxKwW3ngj4AmllT9O4XtJEla94XEC6p6oJSwMPK4vMQzsf0OOWDuGIoTLuUPD+oefvEEqyXtMJxyOJrFW+7S7yEjEXlD6T8g5AfEbPP47sTzoGY9PHt13Rlo4O+DLGvsIYW8MYWzM6Otr0dkLPP6Q3IqNp8lwBXzRL1wMXCIy/VMYg7n2Fnr/BEy4XfF0ck7gbX1cW8OVDRppeKjxPUTWzXi4iCZusmkjTX34eakqiiSrfALJCNZtUxVZ0/dtBiRARzl09hp88vVMqOxF6/kG2DyCXIdFx/gAwcaAzgtxJzAWlz6SIoczh4PZnWjIT7S+6cEhO+7Q7vXa2jP9WAEcK/y8PlrUFPNsnSQOYaB/+zJQLcblcmSMyVFF38xKhbKso22a5YK3vIy3yEl44svRSDnORl/x7qj6+sTxmm25eEgyVPZQ8x9rzl3lhMnkEnY1e2kSVb7sf0PNWj+HAVA2/eH53et8JbR9ATvuozjpLle9seN9zSemzImnizq9YQfD80/EzUhZCRuvkeqghZsv4/wLASiJaQURFAO8E8J127Iix+LRLHMSVgotqnYUBL9XwlgV8kxyvrpWjCF1D9eRN9iUe9Nk+AG+6rhaeqxTT6aWi0YoCfr4HSaTucyAeZ5I2CzYcg0rTnxk4CSLC2FApU7qnKO8A6HsNyLBksASHstE+7cZrf2sRip6D2yTVvvxSO2K2j4z2UVzjxQPZJB5mMuALdL7SZzzOpA74FgXOH0BqzNs2bsobs2L8GWM1AB8GcAuAJwBcxxjb1K79RbRP/OL3WfDbgM/5V+tMOwBVTdyTNq4SBnz1gmj+NuWDIumFlQqOspMXEL1wVEFm0fMnIrlRl0DG+fNtcOiauJvQbC/fUHdIMlvTGTDPdbBkqNyUrn+77GJf0cNrj1kkDfo2xGwfSZ6/6djKBRdDZc/qBTtbtHunK30mn2vZcxMlnMjtx2C5II3tdWvAF4yxHzDGXsUYO4Yx9pm27QcsNu0SoVKmTCLslsWV+aSG2k6CuV8yPeRIPqRDlkHkioLz51Bp73Mk5WZtA+G6YioOVQ2EzcDO0stXTLuz6TKmwtLhbB29ZsIorl89hs07D+K5iQOx5Q3hhRbSPlLOX/1qGstQ5TvDjj+ASOmz06kfXvEuUo3cSYtSzdO0D6B2HDnadd07OuCbF9yYsFuEMK1xWi63Kqp6AvEZQtpQyxu6RIU2FNtnWngsfdymhi4x1VHZCywxwxE9/6SCaXJ7OnmHWJ6/JpYACH18VXpGym/66Z4TzQR8C2r+1YRmC73aSYmcu0re4CWSC1fQPho9f46sVb4zDa70+aPHt3ec0qc47IlIWvcCCKmetUhSXsRQ2Wurbr8K88L4E4JuOkkP11KczKabl62+j+MEg0Tm+Se5QEXAN13ElfTU06megDrDKD0jUvc5iG/XQ73BYlkMfs/R+LZchzIHfAHf898/Vcv80OsKbkzpiuMjvsSDdW/bTEfWHI5c2IdXLRmQGH//tyNk+8iExXQvptFBu7jKbCZbdrLSpzirUtGghUS2YRKm3h29Tl5Ngj8AnkuxSlYgndaoGuBhE/cqnyGk19E1dElCprUjw5BhOsjhZy3JCpr837I+vuIp1BrpLChVe8vYepJq4PQxkFbTX0tJZEj35I3MAaHUPmOePwCMD/u9bW0Kn2YS61cvwf2bd8fGQ6yTl0zbJ/htMv4T++0aubfLCJkwV5Q++4pefHYd/E56/klk6QGeJ7re+AP+4C84Tirgwukck2cppX0Sz4GqoUuykxcg19qRPXpDlQKmavLewEDkxaoDvj4iqkl+njXJS1Fr0JEwsol1k9fGlMqmAk9FzCrwFsU45DSXDlG6Z9agb3sN4/rVY6g1GH7ydBT4DFU9HQjaPukZoG62MzZYwuFq3aiFNJt1Vp2q9CnKiAM6z18uL8PB1QFSSr/dGvCdaXgu+fSGcEFN1axiMRiAlD6NiCyyzpWCvOJWxgXKtpkcE5WCi+laI5zZJAeN1POXcP4cKcE7xSCUZtVI1vM1/eMzGJuBHbZzzNjUJXzZSWguk/MaFXrZ8f4zlf9+6lEjGK4U8Dc/fAK3PeHTH/y+OURhUx+Z5697L0VVvuZrPDt+v4+5oPSp4vyLCnkZjqGKhwZLxwE5egHfJhHRPo7w5o0HXw9Pq+kcwO/kBQi0j2QdVUMXWZmN31lLkfdusc3U8SleTqGMtEFPSNbeUlv8JuT5A2naJzlYtbSPci+R52/bzpFvi7eutElXTYJLPGQN+rabEfHuuwp/uHwLXth9GO/9xkYAQGnLPXif+124DoW55FLOX7Pd0YztHGcLc0Hps5KoeA+bucQkne2f826t8J1hEAoOpd68Ms9VBnm2T7LIy97z7yu6Vl5p1NBFxZf7v5NGODloZLojIqJsn4jOsQ34AgkZW8l4lWn62wzskb4Ciq59la+I5AvWJvMFABb2F1HyHGtd/xljQ5adiiu2/zXOdPxymJc33YpFP3gfHmGvhEsUSjzEHYD4fZWBx1VMjdxnqpOXDlzpc3OHKH0yMb8YQSW9xHv3NNo+gNl2dFuF74yBD1nPdVKpnqY8/3Sqpzr9UdfQJYl+iSaOlPNXKHsmvYcwZTURUBK1fQC51rjnkDnPX/HgR5x//FySxmag5Ck1/XUDm4j8gKTB85d5UzaKo6p9jo9U2t48OzNWrEP1LV/H1YWr8DHvejjffg/ee+hDuK9xPJwglbnkOfI8f81mM3n+s8n7IFL6vKlDlT5VcicFjbYPkE3BN090vfEHfAPjuSQIu/kIOX+Fx8xRDrN9gvUU3i2gKWYSU8JKZk0cIFL8U71Q+PqlRBP35PFxGkQW2HYdiuiwYFnZGPANzsNSQG3Q0M1LhyyFXuKLRBV8s8HS4XJmiYeZsIsDx67HN+vn46PeDbgOr8d9jeMBAC7xwL+bOdtnpFKA55AxqN4JwpqdpvSZiq2lnDp/Bd6LN+l8cqg8/17ANycUHCclz8C5fFvaJ7Ze4oHK0tDFHyRmw2RLJak0+OMZRp60yMtzCHVpnwO1VhCHbSWtjPO3Hdh+L9/mJB4OyugoCyvtF3pZUk0zaYM23433FG/HV+lSXMp+FFJAruj5Z2jmAviZNLYdvWbZ8QfQeUqf4rU1FXnVEsWUHLom7rL180LXG3/uIfBsH9Fj8FwHRdccGJRx/jIoG7ok4HP+EmOYuMtK2iexvWTWksweqQam61Cqw1Cl4CqboSTXA9Ivz3TAt4BD0/VUVhFgHtg2vXxlRq6v6Gkb2OgwPlzGjv2TmcTE2p4Dv/lu4PrLcc2yv8S3+n8ff1H4L7i6cBXOdDbFaR9ZD1/DoWVq5zjL6GSlz0rRn3kls+648VfTPha9O9qArjf+QFDh6zrSCrtywYnoAZb+Hl8HiDIpVPSQrFhDFmjsK3qYqpmNa1/RheuQ0SNIzkzkxtCVBnw910nJO6hiCMn9SnXzNZTYgSbkFpYMlbD3cNUqAC0i+bLL4qCPj1TQYMB2q8rXGXL9t/4SeNs1eLb/FNQZw4Puq/Hh6h/j1fQctr7sU1Qlz5X38DVsenQgm3rqbKKTlT77FU3cxR7iKhkXQJ0p2C7MC+MPAAWHwsEiekLSfq8JFN1A5lij7QOoG7okoWv8IIKI5C8UibwDkA5IxzlwefWh51A6z7+YzB6SQymdnCqAUyt7muQWwmyUjMZJdV9tulE1o+vfdkrk7D8BVqyDG9wvIuC+xvH4x/p/wHeD3PdSQU77mA7OmvaZpQrfJDpJ6TNpS4B06rPrEIgiVc/kGCx5DgouqbN95mMnr7zAA761etpPEzNbVEbOb5JuTn+UavpL9FX6FE3cZbd4SCHrLILPTKLzSJ+J38dX4vk7hOSEKKprUJ2vf6SuQyh6Dg5V9ZW0oeffxLR21KKdoyyw6XP+zdM+gJ3xn+m4o+f441g818+85QQAEtonnHWaXrAl7D44JaXlwm11QICVo1OVPqN6mrQ98RUG5NfQd/LSlHG7r/m8MP6A380ryW0DfmGGOtUzemhiTdwV98RWoyPZ0EV3k/2GLnpdnDAmoZnBVApevIIw2KfrRueYlG0Ir4vm+GRZNUljE2n6R4PbdmAvabKdY18yqJ7hQWpe4qH9cB0HtQaDI4zNV44OAAhoH5GqkzgeMowOltBgwC5Drn+HOP6h0uctm7ZLu7XNFJIihn0K2odAKLgUZPsoqnx1kvC9gG9z0Am7AUDFUsEyKZssexDsA75yukS2TZnnnxxAEUevTkXtL7nSVE/e4lK2PZtUyT6LfHpVGixgEfAd4lW+2QyxutGM+bsDJQ9DZS+Trv9MGcaCS6g3Gvj1rkOpz/w8f3MKcRKjFhpKneP3+7j45HEcrnaW0qcu+81zI22xVmxHnuh64w/4b15PMe0SuWGdc+i3ctQHmGS63BHtGg++AlGPWd1+bWYTZZXMQoJqkgVAeZqguL4qdVS23bJFzETG+dsak4V9RWMeutjLlqOv6KLWYNLG5zaw1fWfaTbEDWif849dkvqsFGg8cdge2qhllW+HOP4AIqXPGzuo4Ctp/MWxUVAknHDIk0Xai3lh/AFE0y5JsNRGA6bkOdpUSsC/gdO1hlYADlB7CDJudqisb+gC2HUkU/UQ8Jz0PssJ2kc3CPuK6euXUvVsgfPneehZs1EqiSl4hjR/ABlz/dG+oFwSBdenfRb2+xkiv71ycfhZOtUzODbDtCRLI/dOQScofSazs2VqsgjWKbiEai1tfzgGy+p+3T1htyYRyhgEDw2Q5PLVxl+86MpuWQIigaZ0ADRZcAWohdZEyJo7yyp4i54j9BtIB/r6FV23PDc9tEyev4i+QlLMKj26W+njC/jGSUf7qOoagOZaOQLA+EjZStlzpukQXpFdb/gKpP/03jPCz1JFXqG2j36bNo3cOyjeG6LTlD51TZM8l5RFXgBv6NIlFb5E9JdEtJWIHgp+3ih89gkieoaIniSiN7TrGKL9IRB2kwR8C65R3gFId7eSeXq2FblJoTVdGf5g2Q/UmnKay146dhFPQ/PAWLohjZbzV8hZxGIm0oBvHDz/WfZStMHooF2f2WS2DyCfgttg6XAFew5Vm5aIaBcKgRZTvdGIUXaAJM/fQtsH8O/hYMkzXuNOSfXk6DSlz75ECrdY41NwHWWRF6DIFAwwVzt5fZ4xdnLw8wMAIKLjALwTwPEALgDwD0TktusAxICvn+qZljKw6fjkyxzLe/1y2Eowq4pBZOBVvtIc+USOsU57iGcYJWcbMtonylc28+U2xVSOo+7mZTOwx4ay0z6qpvW2D1JWXf+Zsouu44AxvyVgyvgXkqme9sc2OqTP9e8EVU8ZZlPpk7F0VTmQpn2ASF5GRxkfmKpp023zxmzQPhcBuJYxNsUY2wzgGQCnt3OHfp6/kxIwA+Jdq4wBXyPtI/FwJRW+lWQ+sDbVk0s8mIO+OpmFpDHk+0waEL4twKx5BMiVDGXGZqCUFHezH+RLBsvYfXBaGbyVcduR598c1WSr6z/T+e+/3uUbuUNTtbCNI0fJ873LRigvYB/p8Kt89TGOzvL7fXSS0meyiVAs4OuR0DdDEtsLnvNmn5Fm0G7j/2EieoSIvk5EC4JlywC8IKyzJViWAhFdQUQbiWjjxMRESwci0/MHIj2OhuyNK2a1FNwwldI/tvTqtn18S57f1DydD6zepinoG3uJST5XNXQROX9Vtk/S6xONbLp5vPz4BsoKz1++egw83dOUjSIi2WIyq5EeDzz/bR2W6//vQWHTHU9OpNI9eStHTi9k8vznkL6PCK70eeODW2f8RZx8LlzH76gmS4DwHBPto37OOzLgS0S3EtFjkp+LAHwJwDEATgawDcD/zrp9xthXGGNrGGNrRkdHmzrGUMaA6/mrpBEMGTpikZdqjEWa/vqALxHFGrrohqyuT4A45ZSloso84WQwypVw/gWX4Dpkl+dvKZ2s0/Q3YSxDq0GOflUtheX3lwyVQQSjrv9MkyGXnX6k8rNSsjLbkvMHfBmNuRbw5XjLKcvw/K5Ds6L0mXyx9kl6dQA821Cu7QPIW7Z2dMCXMXY+Y+wEyc9NjLHtjLE6Y6wB4KuIqJ2tAMQRvDxY1jYQKKbnL0LU49Bd65KVvIN9Ny9ZQxdpFgDv5nVYPyjKBScKXEs5/wTtEywvSGgfIorLXmgDvn6FtDhzUgXDD0yKFb7pY1Qh6uUrN04yPrpV2qfoORgdKGUq9JoJHLt0SPlZSSFAaBPnGB0s4eB0XVsx22Hx3hCdpPRZKUQCiqk8f02Rl9bJm2sVvkS0VPj3LQAeC/7+DoB3ElGJiFYAWAng/nYdB0dMzz9B5wByfjuZ6ilmUsjuB09ptKryLcmbuCdh2yFMVq+QjG0AadonVuQVm0nY9cDlRjasLla8QgdK8mI1m4HdjOeflKhoxomyyfWXzezaCZ0h57TPVFLgz2K7po5eHez4z5rSp7qSPvnckKAqbEoWmTm5inZy/v+TiB4lokcAnAvgYwDAGNsE4DoAjwO4GcCHGGNty6cT9fwbTKeIGR2CPAjqc3a6aLznOuhLNHSRVfgCvuefDDTLHuwhzaCgxEtMxdEDkecf6Qn5y7nWeBJlIQVWB1k+vTLg20SFLwAsGijBIb38gOq4kjRXFiNtm+s/k6hqKpY57TOlkflQISz00sZVOtT1x+wpfSbHUyWhnstRdAVVYcl2ZPHCdr9wvXZtmDH2+5rPPgPgM+3adwoUpTQmc/0jHZsGyoUoAyYlcxysN1XT00N+mbbZ869IGrrIwKtjxUIvmXGX0TSxwjJF0ZMjkXfg24u8+TiS6wHqFpLiecgqfG0qY12HsHhA3dFLdr5ljx9X857U+HAFt/9qhy/gZXhrzFSF719/73HlZ5Hxj8embAO+wNyq8hUhKn2es2ps1o6jX5SLEZ4cX17GIuArUfDtSTo3CTHgC/it1MSLKWtC7kqelkjmmPN28huSrNQLMxASq/cLKZK6HGrXIQyW1KXf4fElWi8mj7GiKCyT5fnz9W0CucmsGn+/6fUGSx4OTNfkWVUWyNLLF/BfamINgk1LwySWjlQwWW1gzyHdte8cQqQkOCiAetYpw6iBWuvkgC8wO0qf6SRuecc8IqDgOdqAr04doF3oeuMP+LdH5fnLOH9O+yRlIJLryaDT6BDRV5JPD5XblOT5x2ISnmto4yiXlFAZ/7Im4Bvfrh23PlD2K4wPWWxThrHBchOyznYFfCosGwly/S2on5ni/E9cNqz8LPT8kzIfFse2sK8I1yEt7dOpAV+OTlD6rKiyfRwKUz1l17HoOSgXHGlG3JwL+HYawibKiVz/MGBZrYcGSVf4JK4ng65MW0R/UZ4VIMNQJS73Klu/UtQHaF2HUPKclDcvy/MHOI2kaOMozpwKdtXKAyVZEYv9wB6zEHdLergxxVa73cSwdNis6z/THvERQfGZDCnaJ8N2HYeweKCooX063PVHZyh99ktmm0CkMKDDoEXjpjzR/cZfkHcAfM9fylkbA77xwLDKZiWlWVX6Ksm2ioDaENooe1YKLuoN5s9sFBapX5hthIFwSZ4/316WgG/8pZI+kaiPr38eWeUCxobK2HVwKlM2R1/BS1MAGbyopYHn30npnq9aMqD8LMz2qaljPzqY1FM73PGfcaVPxmQBX7nWVSFQGNDX9CRtR3tfuG0L+HYSiAgFJ+L8Rch0bHiXpHg2TZzzV8G2KQNvq2hzg4cqntT7TGb7ANFLTPbAy5q4y150QDCIlRW+8W0CZgG1wZK8BsLWoIwNlsAYsPPAdMr7VRm5vpKdXLcKi/tLKLqOsdBLtu924WPnvwq/vXIUp71iQSo2lczzj1hpu4ObS43cVbj45GX40p3P4nsPv4jLz1ox4/vn7UOTz3XBdTBda8BzHeX9GFQ4eR1Z4TsXEAV81dw2EPf8pRr3PJhWrWu9VllDFyAdIO4ren6zkbpYO6AeFGJjeNneY7SU4thkwShPmedvZzhlTaulqZ7l1mWdAX0v3yTiAd/sXpTjEI4YLmslHmaaDPFcB2tfuQgF14llagEyzt9fbk+tqat8ZV5uJ2K2lT77ih4aDDGBPaKgjaMh2cG2DWxe6HrjDwQBXyGfXVb8JMvzF+1FOSkDoXgQMjd0kWh/J6Ht75k8vulGoDYo22eaanIVL0Vb2odfv4jzVxd5ARHnn9Uw8Srf7RmCvpWCZyU6p8P4SNmqo9dMpXrqkKJ9guVZaJ9dB6dnVFmyHZg5pU8moXPlXfV4G0eTiOO+GczznxfGH5DLGABxHZtkwFe8UVloH8BcqdcvZN+YDCEP+CbTJOOB1yTtk94Yp5pEqFM9hQCyNtsnCPjGeh2kEVY/N+v5D6k9f5lyqn9s8r7FWTA+XMG2vZ0T8NUhTfv4sKZ9BkuoNxh2H5xOfdZJ52nCbCp9igKKMXmHQFiSQRfbU3j+vWyf5hBV+MpPlevYiB4iN4iireVFQ+Zsnzi3rQz4ltL58bptNhhSwVoRlWIk6qWipSoFT5CR9peJAd9kkVSNB5A1KBccEEGrK8TPAUh7/rZYPFACETKle0pzrrPtFuMjFby0b3JOeMPl0PPXCxCqYCr06oTZjQ1mSulTtmlZHwke8AWAuibjJxkv7OX55wBfz189cJP8NudSxQc+4tTVJdpA9oYuB6dqxswXlcSDtKJVk43k647Y5fmL2ji6o5O9PGXoV7ZytDMoBdfBov5iRokHr+VOXEtHyqg3mFnrvgPsYsElEPlxKUAUdrP7vp3Ew9zATCl9plU94706OLjzWW1oqnxLHiarjVTfil6Fb4soKDxcwL9hosfMDWJdSvtkU/ZUGXYZN6i6xWFDF80LRWy6rvIYfLnZuGFQxUKSqa0iZNcvRvtITqTg+kUszQZ8Ab+do6wCVZntE9A+jKkrK00YN+T6d1KHKyKKNXG3bePIoavyZWAd8YKzxWwpffYJXfrEsVEIU83TcQIO/pzbZAvmga43/qZsHyDeCAWIUj3jnL99hS+QvoEy3W/A9xCMRV7leDcvabaPF2Ujyfbn79PFoSlLz18IIJtgW0w1UCqkXopZDIpNoVfyuGSZF1nAm7qYgr6dYhdLnpsq8soS8AW6w/OfCaVPhrRXLnXqSKB9NPShreOYF7re+AOcc9PQPkVXWuQl3qgojU4dUAXkDV1kiDj/aD3VQ2rTIcyGpukPPHTRE9bl+YfbM7ydfNpHOA9lyqq8m5ctlgyZWw2KkCmOZoWp0KvTAqG+559Mb7Wz/n1FDwOKRu6ddp42mA2lTzXtY74HqmSRnrxDk4iMnEhvJGQAAu36pEEUX9JEfou2ZLesJJQB38QNjDh/PacOpGkfqbxDKiaRHjF9JV9fR8xY0sk7AIo+B4mTqYh0ksZK+H181edgAs9DT3pPKgGzfsUUPAuGygUMluRFdp2IUsFJ6/lnMB66Kt9Omd3YQlT6nCn0hU2TEtk+Ir2qdBwTrRx7Ad/WQURKegMIctoN8g5ApJlvMnCAmbeTev6KxyscFJpcfx6TOKyhkWR9fHXnatpeuN3E9VMZm6SmP5DNoIwNldBgwK6DdrSEtAAtw/44lmpy/bNSK+1GjPbJyPkDfpXvXJV1TqLdSp++1Hd8WV8hPdv0aZ8snn+CMm7xOFWYF8YfUDctASLOmj/ISoMoKGeqHvZkQxdltS3vrGVR5DUYcv7JOEK8IhfgtI+8yisMRgmzjYJK26eoCfimtivKU6sx0GIFY9TRK26cVOqVedA+QNDRq4P0fXSI0T4Z2jhyjA6VsFNG+2TcTqdgppU+ZRLngN7+cERN3GemyrfrjX+U1SKnNwDfcMry/JMQm7jrIGvokvTqPddByXNwqFpTGi+OlNyrxMKWPCeW5iev8A0GZlXl+auLxnSw1f4flHn+GQzKWNjL146CqSgKbrJi6XBFK/HgozMMYyvZPkB3ef7AzCt9ljwHrkMB1RhBJaAoIqR3D3Pxw/ai640/EAR8NRc/M+1j2J+trLNMaE2FIYPcKxGh7AWBaxPtMxVROapAlNihy3S+yWIqlbEZaDHgq/L8VeiXFdw0YaOXjZSx6+C0dBbUbuXFrCh5bsuc//6pWupl3mGnaY12Kn362T5xEBH6Cm5sRk8gFD3zTRhQiR+2acbV9cbfZOSAqAUif5AdxcUuFdww4Ku7HaJAk86r51o7Ns9VUvdDdgyiEqdsf8k+voDmRRdUDPPtxRq9S9JWk72BZfADvs3n3Yd56AnPVCdkB7RO+3Bdf53MQ6cwIqWC01QPX4653s5RhotPXoZ6g+F7D8+M2FufVEbFbGpdhzBQ8lLJIu1CS8afiN5GRJuIqEFEaxKffYKIniGiJ4noDcLyC4JlzxDRx1vZv/2B6o1/X2A0w5oAJefvGIu8gGyyzraef+yFojB3/vGpNcMrkmCUpzDqouAdY/LWluF+ExXSKk9loOwrmcby7pVbTaPkuVjQV8B2RavBJMSAryojyAZhuqeFwNtsI0b7BMuynHNU5Ts3sptsMNNKn31FL0ypBpCyPzpHQdYJsFNTPR8D8FYAd4sLieg4AO8EcDyACwD8AxG5ROQC+CKACwEcB+CyYN22Q1fhWy66YAxhWXVSKjdcr+D6ks6GN3LcUKsRev4Wb3gT7QPE6xVkD7zo+UfVzPIhIGb7AIDOcekruqjWmVG1UKXpnwVjg2XrQi9V68qsWBYUetno+s824tk+2QvpRlVB9TlW4ZtEO5Q+fZnr9EWpFNLFlEWLgC/Ae4DPgQpfxtgTjLEnJR9dBOBaxtgUY2wzgGcAnB78PMMYe44xNg3g2mDdtsGW9gEij1gX8NWpZnKoNP2T8LV2zB4zwGkfffFHJXw5yQ1wv4QGESWdxc0VXAeeQ6GEtUiFKasaDbMiUdO/mQpfwE/3TNE+YS2F/LgOWaSr6sCbx2hpn+Y3nytKniNo+2RHN1X5iphJpU/+XMcCvpbG32aGnxfaxfkvA/CC8P+WYJlqeVtBIH2qZ0LHRuf522X72L29Ra0d8za9KAtAMSbKYexCblTDNDQh4GsKhB+eboCBaWkfGb0ig6qPbxaMDZYxYUn7lDwHDrUe8C15LhYPlKS5/p0WCPU5/2CMKgoMdVjUX4JDEs6/w84zK9qh9KnaTiWgfTiSCgM6Gk7W0GXW8vyJ6FYiekzy01aPPdj3FUS0kYg2TkxMtLQtT5HSCKSLgVSGrlJwtZLJHIMlD1M1X51P5ZUCgt68RUreUJBBpBu4okaRPNUzTYOoAr5AQvZCWK3ZfPpI07/atNHknn+yt4EMRJTpBavD+EgZL2oDvp3h+8e1fbLn+bsOYZEi3bNDTrFpzJTSZ5+E9rHJ8wfkiR3tgvGIGGPnM8ZOkPzcpPnaVgBHCv8vD5aplqv2/RXG2BrG2JrR0VHToWqhNXKc9qmaaB/X2MAdsNPiAQLO3zbVs+Jhut7QBkv5zERlFl3Hl6gQPeF4ICope+Ea+xf468WziEwaRaLnn5n2GSyh1mB4+ZDQcETz8qxwZc8WXdfx4Yrc8+8wlziu7eMvy2qzu6GXrwwzpfTZx2kfYWjoFAZExDMF23F0EdpF+3wHwDuJqEREKwCsBHA/gF8AWElEK4ioCD8o/J02HUMIIoR9NGWIctr9i656UZQstH2ApECT+g72JwyTzhAOCVW+qi36RWj6mESylaPupWiaSYTriTMnQ6on0HwfX8CnfYB0uqcKsoYuzWDpSBnb9hzuuLz+JEqeH3yvN6LXUjNxlaTnL1OwnGvgSp/fffhFY5MiG/hVz+nlfcV09ltc20e9TU4Zi+OsI7N9iOgtRLQFwJkAvk9EtwAAY2wTgOsAPA7gZgAfYozVGWM1AB8GcAuAJwBcF6zbNogXkRu6VKA0QVvo5B2maw2YGIekuBugoGFKPiVh0yTKpvS7InD+KiQLy3QeSVLtlEMm7wBE109lJOIBX2jXVWFJ0M5RTPfUvTzzon2WjVRwcLquvP6dYhZ5K8cY5Zjx6LqtylfExacsw66D7VX69Gf0TdI+5QKqdaadweeFVrN9bmCMLWeMlRhjSxhjbxA++wxj7BjG2CrG2A+F5T9gjL0q+OwzrezfFnzoK3VsElIGJrGzyWrd+PYGbLp52UsoyBq6JL17G1qKe8Jhe8uY2mkcFSG7SQfx+ukDvvmkegLZPP/D1dbkHYCo0CtJ/XTaRIBLj9sI7akwOljCzgPxuIpMxGwugit93tRG6qev6MddRPVZG2E3QEUZ9yp8W4ZSyoCLmJk8f94g29jEPfLSdc3ZQ4858BJMAV8goH2UWQZuJOymQJL2MaXAmmgkf5vxgLlq1ZLnoOCS7/k3aTV5I3dbzzQlPdHkc2TS9e8Uw1gS+vg2G48YlcVVugS5Kn0y+awqmglHz7WqY14SMynu1vXGX5Zrm/Zw47SFSt5B19pQxJCl58+zb2w48CGLQREGfGWiIwH6E8EoLefP1U6Thloi7wAgNqOQgYhCiQfVtkwoF1wMlb047aML+Bbsq6h1iAq94hk/ner5T9XqTQd8+eyq23L9Odqt9Clr4m7r+UfNoNROXl7oeuMPRF5ZwdCy8LAx20fUu9Hn6gLmt3d/KcGVG4q8gPgLJa1OGj34KlQKcQ5cW/lc0GsFhdtMeDo6tCruBvjqntbibiUvpmLabNBy8UAJnkMdL/FQCseAwBk3QfsA8SpfjT8x59BupU9ZNy/7VE9JvLATA75zDaoqu0rRkvP3slWy7p+sagObYd69lecf9fE1afccnq4rH1Tf84+24VrSPjpUElIQurHK+/hG1yU7/F6+9vo+NnLTJrgO4YjhdFOXVjSD2oGQ9qk2wmlJ5oBvF4q7ichL6VOX7QPEU59VGlpJiPHCjg74zgXEcm0Vho5PlSMdGxPto+f8C4mGLipwz/+gIT/e37fPl+sKQMSGLtpUT0W2T9JIqOoGkusVPV8K4pCN3HXJw4Gp1opYlgyVsT3hlQKKQrqCa6SjbDE+XNEWenUCYrRPsKyZWgogTvuodGzmKtqp9CnSoBy6VHMRtpmCeaDrjT8QGStOcSTHMBEFTcj1tA+fUpuyfYCooYs+4Bv18TWeA1FKNkIVuziseTn1Fd2wngGwyPPnchGG4xM9bN214bRPK7Z4bNBPRbQx6Dzg29DcB1uMa9o5dopdjIx/lOqpimGp0F/y0Fd0u9bzByKlzxvaoPSZTuTwr78X2h8Nvavo2tcOdL3xFzMetJktQk67yiAmNYB0sGnokpwemjBU9nzaR6PtA/hZS6rx1V90cahaRyNM9dRfk3qDodYwNwfpC4PD+nNIBnyb8SZHB0uYrjew1+IBqQQvWJt7ZsLSkQq275tMpUB2EkoFnu0T3WPL4tIYdI3cuwUXnzyOh1tQ+mSMKavKgTjnD9gFffuKLlyHfNsxRyt8OwrcvuiU9cRqVp28A2Br/M39apOev2lomHQ/eMBXDHCm9lnywJgQ3NZUHpYTWVA69AliVjqDnkfAd8lQPNdfJ12cDKq3gvGRCqp1lntHqDwRev7VhjDbaeIFO1DCxH6xkK57Ar4c7VL67JfQPoBd0JdnxO3X1PPkhXlh/Dl4to8sACZ6/ibO/3BVHVDlCMu0efWpZJ2Q87c0hqKyp7/R+FZtAr7RlFT/okttzzAA/Zen+TwGw05F6utiAuekbZq6RGm85loKE8YDaWdR17+z/P4k7dO85y+TeOg2tKr0mSXgC0TMg9nJ8yXh56qkc8fAJuALxD1/lapnOeT8bZu4641h2XNBJEwPDQY2VPZUDIqwWE3L+ceVPfV5/vFWjhyqqa6vYa4fsAOh4mnzAzts5B4EfXWZQ9H55kD7WLRznG3IaZ/mPH+R9mE2gZ85iHYofYa0z1R2zx8ABkv62F5e6HrjDwh5/ibaxzLV0wa8oYsu4Os4FGSj2HL+cdonucmy4OWqs33iaZnWnr/h2MRKWm2qZzkPcTd5L1/VcQFi9XHzjxIv9JIFfTsx4Mu57KaM/2AJ+ydrucRKOhntUPqMsn2a0/cZtGwG1Sq63viL/qVK2A0IRMyM8g72xt+6oUvJy8D5+wFfFSoWtBQ3htz46oxhOVH8poOt/HOk7FkN9m/cdAr9JQ8DJc+K9gkLblot5Yd//fuKLl4Uq3w7jPfhxv+Jbfvwrfv9vknNXOOwyrfLqZ9WlD5V6a+uQyh5Tjjb5GuEAV9jpmAhkHHJdDiZ0fXG30c81UoGUcRMKens2cmyAlFDF9OAChu6WGCwXMDhah21unxUlCxoqaiPr0StM3FOyeKtaD1ZwZqgoaMtYmld3A2I0j0BgdqTHldQap+DB0tEGB+R6/p3Sg48f2E/+Js94bJmDm00MbvqxoAvRzuUPvtLXioGprM/IoYqXq/CN2/oUq0qglevMv6OQyh69lM3QDRyuqIrc5EXIOr7yL1m8RxU2+Lr2HjCYuWz6dgqgXSyyVnhrRz5dWm2MnbUssq3kvD8W32Olg6XY+JundbMxXMIDgG/eml/uEzXglOFbq/yFdEOpc9KwZVw/nb3YSjU9M/tcKToeuMfD/jKhd2AyEgABo17a+NvV6zRX3Stg5GhrLOC+rGhpXSefxJJzSMOeWA1yvbRGfQ8OH8gXeWrgm2LSVssG6mkxN06CUQUSjyIy7IiVeXbZRW+IppV+mSQ5/kDiRl9IuZocngGg3ToRpPyHLboeuMPmIXdgKgVIaAPkHEDa3MDATO94XP+9rQPoH6hFFxHeHHJj69fU1gm6w8AcNrHnOp5qKrv5AWImv7Nc/5ApO/DGNOm0+Zt/JcOV7DzwFTLrRLbCU7/cTST6rmwvwii+eH5A/krffJGTSJ02YYihsoFNFh+Y1aFeWH8OfQVvo7lenZB38FyvPmKruL24JQdBZKifWT1CgbvX0xDMxnecpg2aA6EVYouGAOmDTEOWR/fZjA2VMJktYH9hhdnMvOiVeeV6/pv39u5RrGUmJ0247F7roNF/cVYoVcnveDyRt5Kn30FN/XcZMn2AcyS8K1iHhh/Ud5Bn+rJofX8LdM9bWWds7QZ5LSPTtaA53mrTiFLAFT1otMVtuj2DeTTzQsQqnyFjB/ZfrnoXFURJM+KSNc/HvTtJEYkSfs0i8VCO8dOi23kjWaUPnV9M2LPA9cW47SPRbYPENmOXsC3BYSpVmGqZ/pqiny5LirPC73Mwdm4/r6OG6w1zA3cATPnD8RnMDK4DoXnkESqbiBDdlOf5Yyor+gXtu1vMQAb05w32CXb2ZoNlg7HO3p1oklMev7NYmyoPG9oHyBfpc++kpdalrWVY7vF3bre+MsCvjKIBkI3OytZ5vpHN9DE+WepHTDz5ZUwJqHZZ+D9m4ai5zooWk5VuYCaabvSbl5NIEsv3/ispDU3alxT6NUpSHL+zUKs8vVz2nPZbMciT6XPPknmnc7+iIj6dbe30KulUUJEbyOiTUTUIKI1wvKjiegwET0U/HxZ+Ow0InqUiJ4hoqtoBlIIktoaMsTTJM0BXxMGLHm7fkujCQADRQ9EetqnbKB9AHsv3d9eOkNK2pgmSwFcycN+runfbMB3iOehi5y0Ksid9sKaRbngYmF/MdT1jwK+nWMZ86J9ZI3cux1ZlD591keRwi1x6iJtMT0GE7G9dqFVF+ExAG8FcLfks2cZYycHP+8Xln8JwB8BWBn8XNDiMVijoC3ysntgOBViuoEF10Gl4AoBX73cgg0chzBY8qwauujAjWHymKQzCcvjq1hy/kCg7NmiVzNY8lApuNi+b8pIveRJ+wB6Xf9OQG60z2AJ1ToLnY1OesG1C1zps1W5B9lznTXg29G0D2PsCcbYk7brE9FSAEOMsZ8xX0bv/wG4uJVjMCHewN0fvDIFv3IsQKNGNokHs7hbv8AN2syB/NJvNV9esUhFzWIMbV+KWbY5UDJfFxOICGNDdprzWV6wNlg6XMG2RK5/J1EieRn/USHXv8PaFrQNXOnzpocslD41VFifZEZvTft0QcB3BRE9SER3EdFvB8uWAdgirLMlWCYFEV1BRBuJaOPExETTB5KMttck01hb2kIVLJVBNP4mrR1b2Gr668ClpJPHJHthSGkkQ7aPCQPlQhTkbsGbHBssYce+Sa14HhCPR+SBZYLEQydmweRJ+wDxRu7zAXkofcqehyKXdLZIsS66zuynehLRrUT0mOTnIs3XtgE4ijF2CoD/DOBfiGgo68Exxr7CGFvDGFszOjqa9et8G+HfXLZBpo1j67lGxtBstAbLekMNxD0EG/hqoeptViw4f7GgzQTb69JXED0dQwGcJBOiGYwNla08f17YlpcHtXS4jP1TtRmR3W0GuQV8Q89/0q9m7aSTbCPyUPqUGX9bzx/gvTtmOeDLGDufMXaC5OcmzXemGGO7gr8fAPAsgFcB2ApgubDq8mBZWxEGfLnxl3j+eQd8AUvaR5IPrMNQRaB9JKvbSTxkp33EY2slNgBEuf6qbdmCe/4m5M35Lx2JdP07kQ7Jk/MH5k+VL4et0qdO3iGW/RYMctsKXyA+w59T8g5ENEpEbvD3K+EHdp9jjG0DsI+I1gZZPv8JgPIlkjci2id9Q22NOl/PpvPPULmAuiGHX5YPrANvDG86PqtUz8RKutRRk0SAbZEXEGVCtYqxwTIOTtcjeWqLY8sDy0bSHb06CSLtc9OHzmp6OwMlD+WCM+9oH6B1pc9+Ke1jp+0D+M/5tEVlfStoNdXzLUS0BcCZAL5PRLcEH60D8AgRPQTg2wDezxjbHXz2QQD/B8Az8GcEP2zlGEyQBXylnr817eNfMpOMARBF7XWIDRKLFzzX/VChUjTTUplSPS22B9gHhoG4598KlvB0T4P3b1vXYIuwo5cY9O0gSkT0/HldQjMgIowOluZVwJejVaVPmT3hxaN1i4sp2o520W0tPYWMsRsA3CBZ/m8A/k3xnY0ATmhlv1kRVfgGnr/EcMdoH822uLyDbSvHcJs5ef68AARQBGiD46tr3hD8hWPzQPPticcvOxUnaGAxVWtY5zGrtmWLZKGXScY6r3T1scESXIfw4p7DWBJU/HYSRM5/QV9Bs6YZY4N+lW9eL+y5Aq70eeODW3FwqhbLyuPQFb7Fsn2CdQoejzla2I5Sa/fNBl1f4Ssi9PwlAd+stA9XddSBa3To0G+ZYsoxZJhNcHmHSc3x8RdOcgYkTR0NtmfTCtB2RpGXIeGFXqaOXlliHDbwXAdLBkt4UdD176QceE77nLNqNFOQUQZe5es3K++cc5wJtKL0KaN9uPNpozM1VGn/y7brjb9M3kFG+7hCoxbdGOe0j80NtKF9sgYjhwwvFP5y0vVezUL72GQPRdu1G7AD5XQwrBksCTx/k65/3qmeAKKOXh3Ih3DaJw/OeFTomDbf0IrSZ0Xi1EW0sw1rIMzw52Cef8eAG5iCo7/4NkYxax/f8BgUnmExpsFvRswj0GT76GipLOmlMq0glcEOB7xhtObl+Q9VPBQ9J5R4UJbaZ7hntlg6UsG2vZ3Z1EVs4t4qxgZL2Hu4akXndRtMSp86eQeZpEiYcJKT49gqut74i1k53PNXee02QcusRV4mEFH40rHxgk2ev805yKak/gGkF/GAr81Mx3ZGkdfAJqKgqYveM8072wfwJR627ZkM4widxIiUMlCTJvBc/53z1PtvVulTNqPnqp5WtI+F49gqut74i+DTLlUw1IbisNXzBxJGTrNNWTBJvU072keHZuQdREOizG22SDMFoj6+QOtG0ybXP2tQ3QbjwxVM1xvYZan9PpPIm/YB7NRTuxEmpU/V+C1J5NB1qeZJ9Dz/nKHL9gHsDKetpDNg9tI5Qs/fZpsVfaaMleevMIa6zmBTFtlNti+VvPL8gUQvX2XmRRtonyDLh6t7dpDjHwZ886B9uPHfdXCqo2Y3MwmV0qeu1kc2i/cy0T69bJ+WIcvzV9I+Vpx/vrQPkI2DN9I+hmYuQLacfH5NdNlDye0ai7xK+aR6Ar7nb+pKluV8bdHJuv55ev48nbYD49ozhryUPouh/THfl5iT1wv4No9o2mVJ+2hMUpaAb5y3UyPi/M3bNHnNNqJeWWgmfr7iC1MtoDazqZ6Ar+9jQns4/w42/oX8Ar6LBorh3/PV81cpfWq6OCYQyDuEqZ4Wxr/n+ecLl198BedmxZdnqWS19PyzGOOC62gDxDYGWBXw1ck72MDWyLoO5WaQuf4MoJN3yJ8/XdBXQLngRLRPB1lG7gDk4fkXXAcL+4vmFbscXOnzoRf2NL0NncJAEnkVQurQ/cZfzPN3DJ5/zqmevKELYCe3YGs/dF5BMwFfrQJoMzUBFsOVe/8tB3xtPP+ci7wA/36OD1farr/SDPKkfYDoBdtJhWwzDa70eVMLLR6Lrr3n3+P8c0JKz1/B+Yf54BZFXraw0/fJKvGg1qqx8dSTnrCrscCy7amMQCSjYPZs8hN3Ezx/Vbe0NnD+QFw3p5PMYimD/pQNRoVrPF8hU/pkftmz8bvJHr69PP8ZglzYTf5QWHn+GRtl2NzErJ6pziuwMf5uoqjM1fQWzfKy4zUBuupijkjTvzWzucTC829V4kCFpR2o6wPk18yFY3SgZ/yB7EqfyWcxTDixoH3Elo+9gG8LCAO+jv7NayOH7GSoxgUiQ20V8LU0hDp9n2a03JMvAxFZaC7uYZuyb4D8PP8FfYUwkD/TWCp6/h3k+uel588xGmgoddI5zgayKn0m41qmVPOZxrww/hyeIdWqHSmBVp5/ZtoneKFIHsasLyegCdrHkO1zaNrC+OeU8UNEoWc607aJ6/p3GnI3/j3PH0Ck9HnLpu04aOghAQjy6sH/OmHJ2UDXG/+4vIMp4Jv/5bBJ2craZjBvPtDVeM4xgSrD8XEBNSvPv6R+gWXFqAX10w5wXf9OQ5ZCRBuMDs7Oy7UTkUXpM+X5h7RPVs+/J+/QNJJ6/irOzSYzJyu4odZtMrOmf86ZADwLSnZZxBiHboYARLTPpIXnn+cLbMksBSTjAd/OMY3lvD3/XsA3BFf6vMGC+kn2kfAMtPNMo+uNvzTg24K8Q1a0J9snP68ZiLT6ZTMikUYyafo3Q/vkcQpjs8RJj3co7ZN3gJtX+fYQKX3yoK/WqQuea54AYbI/KvQCvi0gKawkdfzv2YDlezfife53MbL9PgDAmc4m4J4NwOa7/d9NIAr4Ku7gPRswvucX8WWG/bXL8ze1lzMNwvIsBHyB2TNOfUUPI7xTVuc4/rkj9Pzne8Q3AFf6NKEv4Qxx+2OT7QPkWwkvw7ww/gCAezag8Jt74stEI7vsVJx2/8dQg4Nj7/ljvNf9Pq4uXAU4HnD95cCyU6MXgWobEhg9/2Wn4oR7P4oznU0+3bT57mh/zW4zIzjnXzdMR3UzBNyzAYsmfg4AOMw9f821yW1g37MBJ04/HF8m7reJe5YFHcf7532+92zA0Lafho2OWt5eF4ArfQIGKZjQ+NeAezagvOVeAILn36rtaBGtNnD/X0T0KyJ6hIhuIKIR4bNPENEzRPQkEb1BWH5BsOwZIvp4K/u3QejMLjsVzr+9x/fmgbSRXbEOT637e3zQ+w52L30dPun9C+5snATc83fA264BVqzz173+cvU2JDBW6q1Yhy3n/wOuLlyFVY9f5W+P70+BkPZpxt0UjMP73O8Cm+/GH4y/gPe53/W9cYnhfJ/7XZzpbILrEM50NsG77wvpgbvsVBx56wdwprMJh6o19bUJthnFQqg1Y7LsVJz10J8GL0+k99vEPcu0+06jfvI+32Wngr79Hryh7yl/tOV8/eYqLj55XP1hMMZ5Isfh6TrgeBi64fdwprPJ18nSXcfg+9xBqtYbbXnhtur5/xjACYyxVwN4CsAnAICIjgPwTgDHA7gAwD8QkUtELoAvArgQwHEALgvWbSuICFixDvS2a3B14Sp8zLteamSXnvR63D7wZhzx/I2YXnY6LnHvAda8N1pnxTrAsI0kbAK+C48/H98vXohVT34pvj8FlHn+Nl5fYBz+88rteIS9Erj2Xbj8hSvxh++8BAt3/ExqOGtwcHXhKnz3tIfwlfLVKBSK6YG7Yh32vvmruLpwFd49+c/qaxNs85iDvwQAjO78WWvGZMU6bD3/S7i6cBWOfeLv0/tt4p5lAff8O4YRyft8g+19uvo5XHbwn3K/fnMS92zApYueg0NCEWSCRcD1l+MTx07gTa9eiotGngXu+TtU130CVxeuwgfYtfrrmHqB/6QtL9yWjD9j7EeMsVrw788ALA/+vgjAtYyxKcbYZgDPADg9+HmGMfYcY2wawLXBujODFevw3cKF+Kh3g9TILpr4Od7GfgS8+h0ov3g/8Op3ABu/FjeoK9bhx31vUm4jicWBKqIu93p4+334T4XbgHV/lt5fEvdswPI9G0EkCMLxgRcMmj9avgUL+gpy7yJ4mD+y69P45/OmAfh09ejE/UrD+UHvO7izcRJW/PJvMHjCBXDu/bx04PatOhffrJ+PP2x8W31tgm0ef+9H8fMzf45lP/5gy8bkyFPfgCeWvQ2rn/qyfL8r1mHLMZdZ3zMrBC/aVyzqg+uQHzfpFDpkxTpsW/kf8zvfFevwm1dehncc+lZ+128uY9mpWPSD9+HWtzj4j2ccJWUR8LZrsOD7V+CLR/wQlRvfC7ztGhTO/ggeXXoJPuT8u/46Bt//+IG/xce867Hyrg+354XLGMvlB8B3Afxe8PfV/O/g/68BuDT4+T/C8t8HcLVmm1cA2Ahg41FHHcUy4SefZ+y5u9hvdh1kuw9M+cvu/XvGPn0EY7d9mrG/XcHYc3dF6z93l7/s3r+X/+brPncXa/ztCla79b+ntyE5hsazd7Lbf7WdNRqNaD8/+Xx6v8L2tdsNPn/u/h+wWr2h/r7sHEXc9mnGPjXk/xb/lmDbDVf6n3/tDdr12HN3sfr/WMFqP7a4NoZ9ZoLpnG2vSRP7PPzk7ez+zbvM920mkff5tuP6zXXYXJPkGM94HRu3/veWnxEAG5nKvqo+YJEBvhXAY5Kfi4R1PgngBgDEcjT+4s9pp52W7ayTD+O9f8/Yp4b937LPg5dF+Juvk1zehKHWri/uT/ye+IJQbVc1iEyGVfz+Z5f7PybD+W9/5F+/f/sj/Xo21yZPY2Lab9Z71sy+O8ko5n2+7bx+cx265yw5NiROpJXtaHFstWT8TT8ALgdwH4A+YdknAHxC+P8WAGcGP7eo1tP9ZDb+jMUv4KePiAy/+LnOyCbRDkPdLFQDz9YLfu4u/4cbf/6/bICaZkOM2V+bvI2Jab/N3LMsyHMGkwfyPt92X7+5Ct1zJhvjWexPjs9I24w//GDu4wBGE8uPB/AwgBKAFQCeA+AC8IK/VwAoBuscb7Ovpow/Y53xcOZ9DKqBl3Wmwf8WB6HMcOpmQ1nRTcakEz3/HtoP03PW6hjP8Rlpp/F/BsALAB4Kfr4sfPZJAM8CeBLAhcLyN8LPDHoWwCdt99Wy5z9bD2fex6AbeN1kWDsdPTpk/mIOPWc640/+552PNWvWsI0bN9p/gUfgeZQ8+f9MoB3HwLN6xO9vvhvY+kvg7D9p9Yh7sEXvPvQwB0BEDzDG1kg/61rj3wkPZyccQw899DBvMT+Nfw899NDDPIfO+M8fbZ8eeuihhx5C9Ix/Dz300MM8RM/499BDDz3MQ/SMfw899NDDPETP+PfQQw89zEPMmWwfIpoA8OsmvroYwM6cD6eT0O3nB/TOsRvQ7ecHdOY5voIxNir7YM4Y/2ZBRBtVqU7dgG4/P6B3jt2Abj8/YO6dY4/26aGHHnqYh+gZ/x566KGHeYj5YPy/MtsH0GZ0+/kBvXPsBnT7+QFz7By7nvPvoYceeughjfng+ffQQw899JBAz/j30EMPPcxDdK3xJ6ILiOhJInqGiD4+28fTLIjoSCK6g4geJ6JNRPTRYPlCIvoxET0d/F4QLCciuio470eI6NTZPQM7EJFLRA8S0feC/1cQ0c+D8/hXIioGy0vB/88Enx89qwduCSIaIaJvE9GviOgJIjqzC+/hx4Ix+hgRfYuIynP9PhLR14loBxE9JizLfN+I6N3B+k8T0btn41yS6ErjT0QugC8CuBDAcQAuI6LjZveomkYNwH9hjB0HYC2ADwXn8nEAtzHGVgK4Lfgf8M95ZfBzBYAvzfwhN4WPAnhC+P9vAXyeMfZbAF4G8N5g+XsBvBws/3yw3lzAFwDczBhbDeAk+OfaNfeQiJYB+GMAaxhjJ8Bv2/pOzP37eA38drUiMt03IloI4FMAzgBwOoBP8RfGrELV4msu/6CFRvGd/gPgJgC/A7895tJg2VIATwZ//yOAy4T1w/U69QfAcvgP0XoA3wNA8CslveT9BHALgDODv71gPZrtczCc3zCAzcnj7LJ7uAx+S9eFwX35HoA3dMN9BHA0gMeavW8ALgPwj8Ly2Hqz9dOVnj+igcixJVg2pxFMjU8B8HMASxhj24KPXgKwJPh7Lp77BgB/BqAR/L8IwB7GWC34XzyH8PyCz/cG63cyVgCYAPB/A2rr/xBRP7roHjLGtgL4HIDfANgG/748gO66jxxZ71tH3s9uNf5dByIaAPBvAP6EMbZP/Iz57sSczNklojcD2MEYe2C2j6WN8ACcCuBLjLFTABxERBUAmNv3EAACGuMi+C+6cQD9SNMlXYe5fN+61fhvBXCk8P/yYNmcBBEV4Bv+f2aM/XuweDsRLQ0+XwpgR7B8rp37WQB+l4ieB3AtfOrnCwBGiMgL1hHPITy/4PNhALtm8oCbwBYAWxhjPw/+/zb8l0G33EMAOB/AZsbYBGOsCuDf4d/bbrqPHFnvW0fez241/r8AsDLINCjCDzx9Z5aPqSkQEQH4GoAnGGN/J3z0HQA8a+Dd8GMBfPl/CjIP1gLYK0xROw6MsU8wxpYzxo6Gf59uZ4y9C8AdAC4NVkueHz/vS4P1O9rzYoy9BOAFIloVLDoPwOPoknsY4DcA1hJRXzBm+Tl2zX0UkPW+3QLg9US0IJghvT5YNruY7aBDu34AvBHAUwCeBfDJ2T6eFs7jbPjTykcAPBT8vBE+P3obgKcB3ApgYbA+wc90ehbAo/CzL2b9PCzP9RwA3wv+fiWA+wE8A+B6AKVgeTn4/5ng81fO9nFbntvJADYG9/FGAAu67R4C+CsAvwLwGIB/AlCa6/cRwLfgxzCq8Gdw723mvgH4g+BcnwHwntk+L8ZYT96hhx566GE+oltpnx566KGHHjToGf8eeuihh3mInvHvoYceepiH6Bn/HnrooYd5iJ7x76GHHnqYh+gZ/x566KGHeYie8e+hhx56mIf4/wGXNAaFMmKvngAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"start = nap.Ts(ep_fwd['start'].values)\n",
"ends = nap.Ts(ep_fwd['end'].values)\n",
"\n",
"plt.plot(dy.restrict(ep_fwd))\n",
"plt.plot(start.value_from(dy).index.values, start.value_from(dy).values ,'x', label = 'start')\n",
"plt.plot(ends.value_from(dy).index.values, ends.value_from(dy).values ,'*', label = 'end')\n",
"plt.legend(loc = 'upper right')\n"
]
},
{
"cell_type": "markdown",
"id": "a6ede309",
"metadata": {},
"source": [
"This plot shows us that the trials we have are indeed forward trials; y-position at the start of the trial is lower than the y-position at the end of the trial.\n",
"\n",
"Now, you can save these variables as a CSV file, to use with your other analysis scripts.\n",
"\n",
"I hope this tutorial was helpful. If you have any questions, comments or suggestions, please feel free to reach out to the Pynacollada Team! "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
